docs/notebooks/EEG_pipeline_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ae7bc6b5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# EEG Pipeline Tutorial\n",
    "\n",
    "In this notebook, we will demonstrate how to use the newly implemented EEG pipeline of CMP3, using the \"VEPCON\" dataset, available at https://openneuro.org/datasets/ds003505/versions/1.1.1. \n",
    "\n",
    "It is important to note that CMP3 _does not_ include preprocessing of EEG data, so it is expected that you have your data ready to be analyzed. \n",
    "\n",
    ".. nbinfo:: **Important:** Note that the skull-surfaces provided with the dataset (\"bem\", see below) which are needed to create the head model are obtained from non-defaced MRIs. You will not be able to proceed with surfaces created from VEPCON dataset alone."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55891bfc",
   "metadata": {},
   "source": [
    "## Setup instructions\n",
    "\n",
    "If you want to reproduce all the results of this notebook on your side, a conda ``environment.yml`` file can be downloaded at the following link: [EEG_tutorial_environment.yml](EEG_tutorial_environment.yml). The original ``.ipynb`` notebook file can be downloaded at the following link: [EEG_pipeline_tutorial.ipynb](https://github.com/connectomicslab/connectomemapper3/raw/master/docs/notebooks/EEG_pipeline_tutorial.ipynb).\n",
    "\n",
    "Once you have downloaded the conda environment file, install the environment `py37cmp-eeg` as follows:\n",
    "```bash\n",
    "$ conda env create -f /path/to/downloaded/EEG_tutorial_environment.yml\n",
    "```\n",
    "This will install all the packages needed to run this notebook including jupyter lab.\n",
    "\n",
    "You can then activate it, go to the directory where you downloaded the ``EEG_pipeline_tutorial.ipynb``, and launch jupyter lab as follows:\n",
    "\n",
    "```bash\n",
    "$ cd /directory/of/downloaded/EEG_pipeline_tutorial.ipynb\n",
    "$ conda activate py37cmp-eeg\n",
    "$ jupyter lab\n",
    "```\n",
    "\n",
    "You are ready to open and interact with the notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14e8a61a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install ../../."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95d6d81",
   "metadata": {},
   "source": [
    "## Loading the python packages used in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed5bb380",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220709-17:01:15,509 nipype.utils WARNING:\n",
      "\t A newer version (1.8.1) of nipy/nipype is available. You are using 1.7.0\n"
     ]
    }
   ],
   "source": [
    "# General\n",
    "import sys\n",
    "import os\n",
    "import argparse\n",
    "import subprocess\n",
    "import pdb\n",
    "import pickle\n",
    "import shutil\n",
    "import json\n",
    "from IPython.display import SVG, display\n",
    "import warnings\n",
    "\n",
    "# Dataset management\n",
    "import datalad.api as dl\n",
    "\n",
    "# Data/graph handling and visualization\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "# BIDS import\n",
    "from bids import BIDSLayout\n",
    "\n",
    "# MNE imports\n",
    "import mne\n",
    "import mne_connectivity as mnec\n",
    "\n",
    "# CMP3 imports\n",
    "import cmp.project\n",
    "from cmp.info import __version__, __copyright__\n",
    "from cmtklib.util import print_error, print_blue, print_warning\n",
    "from cmtklib.bids.io import (\n",
    "    __nipype_directory__, __cartool_directory__,\n",
    "    __eeglab_directory__, __cmp_directory__\n",
    ")\n",
    "# other\n",
    "from EEG_tutorial_utils import (\n",
    "    create_trans_files,\n",
    "    fix_vepcon_derivatives_dataset_description_files\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb528465-6753-4e1d-b81f-8ecd0e7528cd",
   "metadata": {},
   "source": [
    "## Loading the BIDS dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac595690-4f0e-4a53-8dc3-11486cfbc698",
   "metadata": {},
   "source": [
    "For demonstration, we are going to use the latest version of VEPCON dataset, available on [Open Neuro](https://openneuro.org/datasets/ds003505/versions/1.1.1)\n",
    "that already contains outputs from Connectome Mapper v3.0.3 and Freesurfer 7.1.1. A full description of the dataset can be found in [Pascucci, Tourbier, et al. 2022].\n",
    "\n",
    "In case you want to rerun the notebook, make sure to remove any `ds003505_demo` folder in the directory of the notebook. Otherwise, datalad install will complain.\n",
    "\n",
    "[Pascucci, Tourbier, et al. 2022]: https://doi.org/10.1038/s41597-021-01116-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "902eca4a-da82-45ed-b80c-d0abb09cb92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.95 ms, sys: 13.8 ms, total: 23.7 ms\n",
      "Wall time: 58.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Download example dataset with datalad\n",
    "bids_dir = os.path.join(\".\", \"ds003505_demo\")  # Adjust path to your BIDS dataset as needed\n",
    "vepcon_data = dl.install(\n",
    "    path=bids_dir,\n",
    "    source=\"https://github.com/OpenNeuroDatasets/ds003505.git\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6eaec60",
   "metadata": {},
   "source": [
    "## Running the EEG pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce17d74",
   "metadata": {},
   "source": [
    "As of now, the EEG pipeline can only be run directly from the application programming interface (API) as demonstrated in this notebook. As soon as possible, we will integrate it into the graphical user interface (GUI) and the command line interface (CLI).\n",
    "\n",
    "First, we need to configure the following user-defined arguments. Please modify them as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf09ab4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adjust path to your BIDS dataset as needed\n",
    "bids_dir = vepcon_data.path \n",
    "\n",
    "# Adjust path of the output directory as needed\n",
    "output_dir = os.path.join(bids_dir, 'derivatives')\n",
    "\n",
    "# Adjust the subject to be processed as needed\n",
    "participant_label = 'sub-01'\n",
    "\n",
    "# Adjust the name of the task to be considered\n",
    "task_label = 'faces'\n",
    "\n",
    "# Adjust path to the anatomical pipeline configuration file as needed\n",
    "anat_pipeline_config = os.path.join(bids_dir, 'code', 'ConnectomeMapper-Docker', 'ref_anatomical_config.json')\n",
    "\n",
    "# Adjust path to the MNE-based pipeline configuration file as needed\n",
    "eeg_pipeline_config =  os.path.join('.', 'ref_mne_eeg_config.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1009a870-0d3b-4000-a218-6292c322acb9",
   "metadata": {},
   "source": [
    "The eeg pipeline config .json file contains information that CMP3 needs to correctly load EEG data and associated information like electrode positions, names of conditions, which parcellation to use, etc. as seen below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "967e97c8-03d9-42c1-8c9e-d8438ef28ca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\r\n",
      "    \"Global\": {\r\n",
      "        \"process_type\": \"EEG\",\r\n",
      "        \"subjects\": [\r\n",
      "            \"sub-01\"\r\n",
      "        ],\r\n",
      "        \"subject\": \"sub-01\",\r\n",
      "        \"version\": \"v3.1.0\"\r\n",
      "    },\r\n",
      "    \"eeg_preprocessing_stage\": {\r\n",
      "        \"task_label\": \"faces\",\r\n",
      "        \"eeg_ts_file.extension\": \"set\",\r\n",
      "        \"eeg_ts_file.toolbox_derivatives_dir\": \"eeglab-v14.1.1\",\r\n",
      "        \"eeg_ts_file.datatype\": \"eeg\",\r\n",
      "        \"eeg_ts_file.suffix\": \"eeg\",\r\n",
      "        \"eeg_ts_file.desc\": \"preproc\",\r\n",
      "        \"eeg_ts_file.task\": \"faces\",\r\n",
      "        \"events_file.datatype\": \"eeg\",\r\n",
      "        \"events_file.suffix\": \"events\",\r\n",
      "        \"events_file.extension\": \"tsv\",\r\n",
      "        \"events_file.task\": \"faces\",\r\n",
      "        \"electrodes_file_fmt\": \"Cartool\",\r\n",
      "        \"cartool_electrodes_file.toolbox_derivatives_dir\": \"cartool-v3.80\",\r\n",
      "        \"cartool_electrodes_file.datatype\": \"eeg\",\r\n",
      "        \"cartool_electrodes_file.suffix\": \"eeg\",\r\n",
      "        \"cartool_electrodes_file.extension\": \"xyz\",\r\n",
      "        \"t_min\": -0.2,\r\n",
      "        \"t_max\": 0.5\r\n",
      "    },\r\n",
      "    \"eeg_source_imaging_stage\": {\r\n",
      "        \"esi_tool\": \"MNE\",\r\n",
      "        \"mne_apply_electrode_transform\": true,\r\n",
      "        \"mne_electrode_transform_file.toolbox_derivatives_dir\": \"cmp-v3.0.3\",\r\n",
      "        \"mne_electrode_transform_file.datatype\": \"eeg\",\r\n",
      "        \"mne_electrode_transform_file.suffix\": \"trans\",\r\n",
      "        \"mne_electrode_transform_file.extension\": \"fif\",\r\n",
      "        \"parcellation_cmp_dir\": \"cmp-v3.0.3\",\r\n",
      "        \"parcellation_scheme\": \"Lausanne2018\",\r\n",
      "        \"lausanne2018_parcellation_res\": \"scale1\",\r\n",
      "        \"mne_esi_method\": \"sLORETA\",\r\n",
      "        \"mne_esi_method_snr\": 3.0\r\n",
      "    },\r\n",
      "    \"eeg_connectome_stage\": {\r\n",
      "        \"connectivity_metrics\": [\r\n",
      "            \"coh\",\r\n",
      "            \"cohy\",\r\n",
      "            \"imcoh\",\r\n",
      "            \"plv\",\r\n",
      "            \"ciplv\",\r\n",
      "            \"ppc\",\r\n",
      "            \"pli\",\r\n",
      "            \"wpli\",\r\n",
      "            \"wpli2_debiased\"\r\n",
      "        ],\r\n",
      "        \"output_types\": [\r\n",
      "            \"tsv\",\r\n",
      "            \"gpickle\",\r\n",
      "            \"mat\",\r\n",
      "            \"graphml\"\r\n",
      "        ]\r\n",
      "    },\r\n",
      "    \"Multi-processing\": {\r\n",
      "        \"number_of_cores\": 1\r\n",
      "    }\r\n",
      "}\r\n"
     ]
    }
   ],
   "source": [
    "%cat ref_mne_eeg_config.json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a1eeab-ca5d-4a38-9e73-646217135797",
   "metadata": {},
   "source": [
    ".. nbinfo:: **Note:** If you would like to run another subject (all available subjects except subjects 5 and 15 can be run), you will need to modify the config files (replacing `sub-<label>` accordingly)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128dcf05-36e6-47eb-88b4-16bdc46a4cee",
   "metadata": {},
   "source": [
    "Then, we need to tell datalad to download the actual content of the structural MRI and EEG files that will be input to the pipelines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3618c356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.09 ms, sys: 9.96 ms, total: 17 ms\n",
      "Wall time: 135 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'action': 'get',\n",
       "  'path': '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/anat',\n",
       "  'type': 'directory',\n",
       "  'refds': '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo',\n",
       "  'status': 'notneeded',\n",
       "  'message': ('nothing to get from %s',\n",
       "   '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/anat')}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Raw MRI\n",
    "vepcon_data.get(f'{participant_label}/anat/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e6700a6-5a49-4b3c-8a73-cdd17d7d1725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.82 ms, sys: 10.2 ms, total: 18.1 ms\n",
      "Wall time: 165 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'action': 'get',\n",
       "  'path': '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01',\n",
       "  'type': 'directory',\n",
       "  'refds': '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo',\n",
       "  'status': 'notneeded',\n",
       "  'message': ('nothing to get from %s',\n",
       "   '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01')}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# CMP3 and Freesurfer derivatives\n",
    "vepcon_data.get(f'derivatives/cmp-v3.0.3/{participant_label}/anat/')\n",
    "vepcon_data.get(f'derivatives/freesurfer-7.1.1/{participant_label}/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d22b9b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.1 ms, sys: 14.5 ms, total: 25.6 ms\n",
      "Wall time: 223 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'action': 'get',\n",
       "  'path': '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set',\n",
       "  'type': 'file',\n",
       "  'refds': '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo',\n",
       "  'status': 'notneeded',\n",
       "  'message': 'already present'}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Electrode position\n",
    "vepcon_data.get(f'derivatives/{__cartool_directory__}/{participant_label}/' +\n",
    "                f'eeg/{participant_label}_eeg.xyz')\n",
    "# Preprocessed EEG in EEGLab .fdt/.set format\n",
    "vepcon_data.get(f'derivatives/{__eeglab_directory__}/{participant_label}/eeg/' +\n",
    "                f'{participant_label}_task-faces_desc-preproc_eeg.fdt')\n",
    "vepcon_data.get(f'derivatives/{__eeglab_directory__}/{participant_label}/eeg/' +\n",
    "                f'{participant_label}_task-faces_desc-preproc_eeg.set')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8589509c-a49c-4f39-9a7c-3aa2698f05bc",
   "metadata": {},
   "source": [
    "In the latest version of VEPCON (``v1.1.1``) that we use in this tutorial, we notice that `dataset_description.json` files in the `derivatives/cartool-v3.80` and `derivatives/eeglab-v14.1.1` are invalid and will create an error if these directories are added to the BIDSLayout representation of the VEPCON dataset. We need to fix them by running the following helper function provided along this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8030c331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replace /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cartool-v3.80/dataset_description.json\n",
      "Replace /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/dataset_description.json\n"
     ]
    }
   ],
   "source": [
    "fix_vepcon_derivatives_dataset_description_files(os.path.abspath(bids_dir))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f83c91",
   "metadata": {},
   "source": [
    "We can then configure a new CMP3 project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8590521-2e1d-42f1-b0dc-c0165025d6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize project\n",
    "project = cmp.project.ProjectInfo()\n",
    "project.base_directory = os.path.abspath(bids_dir)\n",
    "project.output_directory = os.path.abspath(output_dir)\n",
    "project.subjects = [\"{}\".format(participant_label)]\n",
    "project.subject = \"{}\".format(participant_label)\n",
    "\n",
    "# VEPCON dataset does not have a subject/sessions structure\n",
    "project.subject_sessions = [\"\"]\n",
    "project.subject_session = \"\"\n",
    "\n",
    "# Set the path to the anatomical pipeline configuration file\n",
    "project.anat_config_file = os.path.abspath(anat_pipeline_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a58fdc9-5556-4332-b56f-2af0c85af1cb",
   "metadata": {},
   "source": [
    "As the dataset is in [BIDS](https://bids.neuroimaging.io/), we can use [Pybids](https://bids-standard.github.io/pybids/) to help us with the task of interacting with the files of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07f4da76-7bcb-4c08-94b0-e2873fe64539",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Represent the BIDS dataset as a PyBIDS BIDSLayout\n",
    "bids_layout = BIDSLayout(project.base_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb8f4bb",
   "metadata": {},
   "source": [
    "Once set, we can run the anatomical pipeline, in order to obtain, among other things, Freesurfer derivatives necessary for the MNE pipeline.\n",
    "\n",
    "Freesurfer and CMP3 derivatives are indeed provided with the VEPCON dataset, so we do not need to run it, but if run on a fresh dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04960911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  .. LOAD: Load anatomical config file : /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/code/ConnectomeMapper-Docker/ref_anatomical_config.json\n",
      "  .. WARNING: CMP3 version used to generate the configuration files (v3.0.2)  and version of CMP3 used (v3.1.0) differ\n",
      "**** Check Inputs  ****\n",
      "> Looking in /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo for....\n",
      "/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/anat/sub-01_T1w.nii.gz\n",
      "... t1_file : /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/anat/sub-01_T1w.nii.gz\n",
      "/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/anat/sub-01_T1w.json\n",
      "... t1_json_file : /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/anat/sub-01_T1w.json\n",
      "Inputs check finished successfully. \n",
      "Only anatomical data (T1) available.\n",
      "CPU times: user 29.6 ms, sys: 6.12 ms, total: 35.7 ms\n",
      "Wall time: 33.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Do not run again the anatomical pipeline\n",
    "# You will have to set it to True on a fresh dataset\n",
    "run = False\n",
    "\n",
    "# Initialize the anatomical pipeline reading the configuration file\n",
    "anat_pipeline = cmp.project.init_anat_project(project, False)\n",
    "\n",
    "if anat_pipeline is not None:\n",
    "    # Check if inputs to anatomical pipeline are valid\n",
    "    anat_valid_inputs = anat_pipeline.check_input(bids_layout, gui=False)\n",
    "    if anat_valid_inputs:\n",
    "        if run:\n",
    "            print(\">> Process anatomical pipeline\")\n",
    "            anat_pipeline.process()\n",
    "    else:\n",
    "        print_error(\"  .. ERROR: Invalid inputs\")\n",
    "        exit_code = 1\n",
    "\n",
    "# Check if outputs to anatomical pipeline are valid\n",
    "if run:\n",
    "    anat_valid_outputs, msg = anat_pipeline.check_output()\n",
    "else:\n",
    "    anat_valid_outputs = True\n",
    "\n",
    "# Set the freesurfer subjects directory and the subject id\n",
    "project.freesurfer_subjects_dir = anat_pipeline.stages['Segmentation'].config.freesurfer_subjects_dir\n",
    "project.freesurfer_subject_id = anat_pipeline.stages['Segmentation'].config.freesurfer_subject_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d109ed82-d59b-4b4a-95da-df5f59c0bfd2",
   "metadata": {},
   "source": [
    "In VEPCON, the electrode positions are provided in a file in the Cartool-derivatives folder, but CMP3 expects them in the EEGLAB-derivatives folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a2cda7a-7999-483b-8a57-b4f0fc72f003",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be Removed !!!\n",
    "# Copy the file to the appropriate location\n",
    "#cartool_file_location = os.path.join(\n",
    "#    bids_dir, 'derivatives', __cartool_directory__,\n",
    "#    participant_label,'eeg', participant_label + '_eeg.xyz'\n",
    "#)\n",
    "#eeglab_file_location = os.path.join(\n",
    "#    bids_dir, 'derivatives', 'eeglab-v14.1.1',\n",
    "#    participant_label, 'eeg', participant_label + '_eeg.xyz')\n",
    "\n",
    "# if not os.path.exists(eeglab_file_location):\n",
    "#    _ = shutil.copyfile(cartool_file_location, eeglab_file_location)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a25d60-4d9d-468c-9334-cd26e3732d1b",
   "metadata": {},
   "source": [
    "Since we are using non-defaced MRIs, which are not exactly the same as the ones provided on OpenNeuro, we need an additional transform that will be applied to the electrode positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23769344-427d-43be-b8e4-789165d98557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting existing file.\n"
     ]
    }
   ],
   "source": [
    "# The following line creates the appropriate file with this transform in derivatives/cmp-v3.0.3:\n",
    "create_trans_files(bids_dir, participant_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4d29a6",
   "metadata": {},
   "source": [
    "Finally, you can run the EEG pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4e3d210",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Check Inputs ****\n",
      "Base dir: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline\n",
      " .. DEBUG : Generated file name = sub-01_atlas-L2018_res-scale1_dseg.nii.gz\n",
      " .. DEBUG : Generated file name = sub-01_atlas-L2018_res-scale1_dseg.nii.gz\n",
      "cmp-v3.0.3\n",
      "220709-17:01:27,860 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_check_input\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_check_input\".\n",
      "220709-17:01:27,869 nipype.workflow INFO:\n",
      "\t [Node] Executing \"eeg_check_input\" <nipype.interfaces.io.BIDSDataGrabber>\n",
      "Load dataset_description for: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3\n",
      "Load dataset_description for: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1\n",
      "Load dataset_description for: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cartool-v3.80\n",
      "220709-17:01:36,614 nipype.workflow INFO:\n",
      "\t [Node] Finished \"eeg_check_input\", elapsed time 8.742065s.\n",
      "\t.. Input file for \"eeg_ts_file\" key: ['/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set']\n",
      "\t.. Input file for \"events_file\" key: ['/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/eeg/sub-01_task-faces_events.tsv']\n",
      "\t.. Input file for \"electrodes_file\" key: ['/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/eeg/sub-01_task-faces_electrodes.tsv']\n",
      "\t.. Input file for \"roi_volume_file\" key: ['/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/anat/sub-01_atlas-L2018_res-scale1_dseg.nii.gz', '/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/anat/sub-01_space-DWI_atlas-L2018_res-scale1_dseg.nii.gz']\n",
      "  .. LOAD: Load EEG config file : /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ref_mne_eeg_config.json\n",
      "  .. INFO: Generated with the same CMP3 version\n",
      "{'Global': {'process_type': 'EEG', 'subjects': ['sub-01'], 'subject': 'sub-01', 'version': 'v3.1.0'}, 'eeg_preprocessing_stage': {'task_label': 'faces', 'eeg_ts_file.extension': 'set', 'eeg_ts_file.toolbox_derivatives_dir': 'eeglab-v14.1.1', 'eeg_ts_file.datatype': 'eeg', 'eeg_ts_file.suffix': 'eeg', 'eeg_ts_file.desc': 'preproc', 'eeg_ts_file.task': 'faces', 'events_file.datatype': 'eeg', 'events_file.suffix': 'events', 'events_file.extension': 'tsv', 'events_file.task': 'faces', 'electrodes_file_fmt': 'Cartool', 'cartool_electrodes_file.toolbox_derivatives_dir': 'cartool-v3.80', 'cartool_electrodes_file.datatype': 'eeg', 'cartool_electrodes_file.suffix': 'eeg', 'cartool_electrodes_file.extension': 'xyz', 't_min': -0.2, 't_max': 0.5}, 'eeg_source_imaging_stage': {'esi_tool': 'MNE', 'mne_apply_electrode_transform': True, 'mne_electrode_transform_file.toolbox_derivatives_dir': 'cmp-v3.0.3', 'mne_electrode_transform_file.datatype': 'eeg', 'mne_electrode_transform_file.suffix': 'trans', 'mne_electrode_transform_file.extension': 'fif', 'parcellation_cmp_dir': 'cmp-v3.0.3', 'parcellation_scheme': 'Lausanne2018', 'lausanne2018_parcellation_res': 'scale1', 'mne_esi_method': 'sLORETA', 'mne_esi_method_snr': 3.0}, 'eeg_connectome_stage': {'connectivity_metrics': ['coh', 'cohy', 'imcoh', 'plv', 'ciplv', 'ppc', 'pli', 'wpli', 'wpli2_debiased'], 'output_types': ['tsv', 'gpickle', 'mat', 'graphml']}, 'Multi-processing': {'number_of_cores': 1}}\n",
      ">> Process EEG pipeline\n",
      "220709-17:01:36,634 nipype.interface INFO:\n",
      "\t **** Processing ****\n",
      " .. DEBUG : Generated file name = sub-01_atlas-L2018_res-scale1_dseg.nii.gz\n",
      " .. DEBUG : Generated file path (no extension) = /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/eeg/sub-01_task-faces_events\n",
      "  .. DEBUG: Event_ids for Epochs extraction: {'SCRAMBLED': '0', 'FACES': '1'}\n",
      "220709-17:01:37,164 nipype.workflow INFO:\n",
      "\t Generated workflow graph: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/graph.svg (graph2use=colored, simple_form=True).\n",
      "220709-17:01:37,205 nipype.workflow INFO:\n",
      "\t Workflow eeg_pipeline settings: ['check', 'execution', 'logging', 'monitoring']\n",
      "220709-17:01:37,219 nipype.workflow INFO:\n",
      "\t Running in parallel.\n",
      "220709-17:01:37,222 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:01:37,344 nipype.workflow INFO:\n",
      "\t [Node] Outdated cache found for \"eeg_pipeline.eeg_datasource\".\n",
      "220709-17:01:37,365 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_datasource\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_datasource\".\n",
      "220709-17:01:37,371 nipype.workflow INFO:\n",
      "\t [Node] Outdated cache found for \"eeg_pipeline.eeg_datasource\".\n",
      "220709-17:01:37,380 nipype.workflow INFO:\n",
      "\t [Node] Executing \"eeg_datasource\" <nipype.interfaces.io.BIDSDataGrabber>\n",
      "220709-17:01:39,229 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 2 jobs ready. Free memory (GB): 14.20/14.40, Free processors: 0/1.\n",
      "                     Currently running:\n",
      "                       * eeg_pipeline.eeg_datasource\n",
      "Load dataset_description for: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1\n",
      "Load dataset_description for: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cartool-v3.80\n",
      "Load dataset_description for: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3\n",
      "220709-17:01:46,693 nipype.workflow INFO:\n",
      "\t [Node] Finished \"eeg_datasource\", elapsed time 9.308715s.\n",
      "220709-17:01:47,241 nipype.workflow INFO:\n",
      "\t [Job 0] Completed (eeg_pipeline.eeg_datasource).\n",
      "220709-17:01:47,249 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:01:47,431 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_source_imaging_stage.mne_createbem\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createbem\".\n",
      "220709-17:01:47,437 nipype.workflow INFO:\n",
      "\t [Node] Executing \"mne_createbem\" <cmtklib.interfaces.mne.CreateBEM>\n",
      "Creating the BEM geometry...\n",
      "Going from 5th to 4th subdivision of an icosahedron (n_tri: 20480 -> 5120)\n",
      "Going from 5th to 4th subdivision of an icosahedron (n_tri: 20480 -> 5120)\n",
      "Going from 5th to 4th subdivision of an icosahedron (n_tri: 20480 -> 5120)\n",
      "outer skin  CM is  -1.05  -8.88  11.57 mm\n",
      "outer skull CM is  -1.05  -8.80  11.09 mm\n",
      "inner skull CM is  -1.05 -10.32  19.78 mm\n",
      "Checking that surface outer skull is inside surface outer skin  ...\n",
      "220709-17:01:49,241 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 2 jobs ready. Free memory (GB): 14.20/14.40, Free processors: 0/1.\n",
      "                     Currently running:\n",
      "                       * eeg_pipeline.eeg_source_imaging_stage.mne_createbem\n",
      "Checking that surface inner skull is inside surface outer skull ...\n",
      "Checking distance between outer skin  and outer skull surfaces...\n",
      "Minimum distance between the outer skin  and outer skull surfaces is approximately    1.6 mm\n",
      "Checking distance between outer skull and inner skull surfaces...\n",
      "Minimum distance between the outer skull and inner skull surfaces is approximately    1.8 mm\n",
      "Surfaces passed the basic topology checks.\n",
      "Complete.\n",
      "\n",
      "Approximation method : Linear collocation\n",
      "\n",
      "Three-layer model surfaces loaded.\n",
      "Computing the linear collocation solution...\n",
      "    Matrix coefficients...\n",
      "        outer skin  (2562) -> outer skin  (2562) ...\n",
      "        outer skin  (2562) -> outer skull (2562) ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        outer skin  (2562) -> inner skull (2562) ...\n",
      "        outer skull (2562) -> outer skin  (2562) ...\n",
      "        outer skull (2562) -> outer skull (2562) ...\n",
      "        outer skull (2562) -> inner skull (2562) ...\n",
      "        inner skull (2562) -> outer skin  (2562) ...\n",
      "        inner skull (2562) -> outer skull (2562) ...\n",
      "        inner skull (2562) -> inner skull (2562) ...\n",
      "    Inverting the coefficient matrix...\n",
      "IP approach required...\n",
      "    Matrix coefficients (homog)...\n",
      "        inner skull (2562) -> inner skull (2562) ...\n",
      "    Inverting the coefficient matrix (homog)...\n",
      "    Modify the original solution to incorporate IP approach...\n",
      "        Combining...\n",
      "        Scaling...\n",
      "Solution ready.\n",
      "BEM geometry computations complete.\n",
      "220709-17:02:50,820 nipype.workflow INFO:\n",
      "\t [Node] Finished \"mne_createbem\", elapsed time 63.377566s.\n",
      "220709-17:02:51,340 nipype.workflow INFO:\n",
      "\t [Job 1] Completed (eeg_pipeline.eeg_source_imaging_stage.mne_createbem).\n",
      "220709-17:02:51,346 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 2 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:02:51,455 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_source_imaging_stage.mne_createsrc\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createsrc\".\n",
      "220709-17:02:51,460 nipype.workflow INFO:\n",
      "\t [Node] Executing \"mne_createsrc\" <cmtklib.interfaces.mne.CreateSrc>\n",
      "Setting up the source space with the following parameters:\n",
      "\n",
      "SUBJECTS_DIR = /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1\n",
      "Subject      = sub-01\n",
      "Surface      = white\n",
      "Octahedron subdivision grade 6\n",
      "\n",
      ">>> 1. Creating the source space...\n",
      "\n",
      "Doing the octahedral vertex picking...\n",
      "Loading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/lh.white...\n",
      "Mapping lh sub-01 -> oct (6) ...\n",
      "    Triangle neighbors and vertex normals...\n",
      "Loading geometry from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/lh.sphere...\n",
      "Setting up the triangulation for the decimated surface...\n",
      "loaded lh.white 4098/149863 selected to source space (oct = 6)\n",
      "\n",
      "Loading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/rh.white...\n",
      "Mapping rh sub-01 -> oct (6) ...\n",
      "    Triangle neighbors and vertex normals...\n",
      "Loading geometry from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/rh.sphere...\n",
      "Setting up the triangulation for the decimated surface...\n",
      "loaded rh.white 4098/147183 selected to source space (oct = 6)\n",
      "\n",
      "Calculating source space distances (limit=inf mm)...\n",
      "220709-17:02:53,344 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 1 jobs ready. Free memory (GB): 14.20/14.40, Free processors: 0/1.\n",
      "                     Currently running:\n",
      "                       * eeg_pipeline.eeg_source_imaging_stage.mne_createsrc\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "You are now one step closer to computing the gain matrix\n",
      "    Write a source space...\n",
      "    [done]\n",
      "    Write a source space...\n",
      "    [done]\n",
      "    2 source spaces written\n",
      "220709-17:13:09,333 nipype.workflow INFO:\n",
      "\t [Node] Finished \"mne_createsrc\", elapsed time 617.869614s.\n",
      "220709-17:13:10,258 nipype.workflow INFO:\n",
      "\t [Job 2] Completed (eeg_pipeline.eeg_source_imaging_stage.mne_createsrc).\n",
      "220709-17:13:10,264 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:13:10,360 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_preprocessing_stage.eeglab2fif\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preprocessing_stage/eeglab2fif\".\n",
      "220709-17:13:10,367 nipype.workflow INFO:\n",
      "\t [Node] Executing \"eeglab2fif\" <cmtklib.interfaces.mne.EEGLAB2fif>\n",
      "\n",
      "eeg_ts_file = /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set\n",
      "electrodes_file = /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cartool-v3.80/sub-01/eeg/sub-01_eeg.xyz\n",
      "event_ids = {'SCRAMBLED': '0', 'FACES': '1'}\n",
      "events_file = /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/sub-01/eeg/sub-01_task-faces_events.tsv\n",
      "out_epochs_fif_fname = epo.fif\n",
      "t_max = 0.5\n",
      "t_min = -0.2\n",
      "\n",
      "Extracting parameters from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set...\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "588 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Ready.\n",
      "Applying baseline correction (mode: mean)\n",
      "Adding average EEG reference projection.\n",
      "1 projection items deactivated\n",
      "Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it.\n",
      ".. INFO: montage_fname = /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cartool-v3.80/sub-01/eeg/sub-01_eeg.xyz\n",
      "\t.. INFO: Create montage from Cartool electrodes file...\n",
      "220709-17:13:11,693 nipype.workflow INFO:\n",
      "\t [Node] Finished \"eeglab2fif\", elapsed time 1.322598s.\n",
      "220709-17:13:12,258 nipype.workflow INFO:\n",
      "\t [Job 3] Completed (eeg_pipeline.eeg_preprocessing_stage.eeglab2fif).\n",
      "220709-17:13:12,264 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 2 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:13:12,361 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_source_imaging_stage.mne_createcov\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createcov\".\n",
      "220709-17:13:12,366 nipype.workflow INFO:\n",
      "\t [Node] Executing \"mne_createcov\" <cmtklib.interfaces.mne.CreateCov>\n",
      "Reading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preprocessing_stage/eeglab2fif/epo.fif ...\n",
      "    Read a total of 1 projection items:\n",
      "        Average EEG reference (1 x 128)  idle\n",
      "    Found the data of interest:\n",
      "        t =    -200.00 ...     500.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "588 matching events found\n",
      "No baseline correction applied\n",
      "Created an SSP operator (subspace dimension = 1)\n",
      "1 projection items activated\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 3.2e-11 (2.2e-16 eps * 128 dim * 1.1e+03  max singular value)\n",
      "    Estimated rank (eeg): 127\n",
      "    EEG: rank 127 computed from 128 data channels with 1 projector\n",
      "    Created an SSP operator (subspace dimension = 1)\n",
      "    Setting small EEG eigenvalues to zero (without PCA)\n",
      "Reducing data rank from 128 -> 127\n",
      "Estimating covariance using SHRUNK\n",
      "220709-17:13:14,260 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 1 jobs ready. Free memory (GB): 14.20/14.40, Free processors: 0/1.\n",
      "                     Currently running:\n",
      "                       * eeg_pipeline.eeg_source_imaging_stage.mne_createcov\n",
      "Done.\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Using cross-validation to select the best estimator.\n",
      "Number of samples used : 29988\n",
      "log-likelihood on unseen data (descending order):\n",
      "   shrunk: 71.873\n",
      "   empirical: -373.964\n",
      "selecting best estimator: shrunk\n",
      "[done]\n",
      "220709-17:13:15,592 nipype.workflow INFO:\n",
      "\t [Node] Finished \"mne_createcov\", elapsed time 3.22245s.\n",
      "220709-17:13:16,262 nipype.workflow INFO:\n",
      "\t [Job 4] Completed (eeg_pipeline.eeg_source_imaging_stage.mne_createcov).\n",
      "220709-17:13:16,270 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220709-17:13:16,364 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_source_imaging_stage.mne_createfwd\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createfwd\".\n",
      "220709-17:13:16,370 nipype.workflow INFO:\n",
      "\t [Node] Executing \"mne_createfwd\" <cmtklib.interfaces.mne.CreateFwd>\n",
      "Reading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preprocessing_stage/eeglab2fif/epo.fif ...\n",
      "    Read a total of 1 projection items:\n",
      "        Average EEG reference (1 x 128)  idle\n",
      "    Found the data of interest:\n",
      "        t =    -200.00 ...     500.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "588 matching events found\n",
      "No baseline correction applied\n",
      "Created an SSP operator (subspace dimension = 1)\n",
      "1 projection items activated\n",
      "Source space          : /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createsrc/src.fif\n",
      "MRI -> head transform : /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_trans.fif\n",
      "Measurement data      : instance of Info\n",
      "Conductor model   : /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createbem/bem.fif\n",
      "Accurate field computations\n",
      "Do computations in head coordinates\n",
      "Free source orientations\n",
      "\n",
      "Reading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createsrc/src.fif...\n",
      "Read 2 source spaces a total of 8196 active source locations\n",
      "\n",
      "Coordinate transformation: MRI (surface RAS) -> head\n",
      "     1.000000  0.000000  0.000000       0.00 mm\n",
      "     0.000000  1.000000  0.000000       9.00 mm\n",
      "     0.000000  0.000000  1.000000     -11.00 mm\n",
      "     0.000000  0.000000  0.000000       1.00\n",
      "\n",
      "Read 128 EEG channels from info\n",
      "Head coordinate coil definitions created.\n",
      "Source spaces are now in head coordinates.\n",
      "\n",
      "Setting up the BEM model using /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createbem/bem.fif...\n",
      "\n",
      "Loading surfaces...\n",
      "\n",
      "Loading the solution matrix...\n",
      "\n",
      "Three-layer model surfaces loaded.\n",
      "Loaded linear_collocation BEM solution from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createbem/bem.fif\n",
      "Employing the head->MRI coordinate transform with the BEM model.\n",
      "BEM model bem.fif is now set up\n",
      "\n",
      "Source spaces are in head coordinates.\n",
      "Checking that the sources are inside the surface (will take a few...)\n",
      "    Skipping interior check for 1736 sources that fit inside a sphere of radius   53.7 mm\n",
      "    Skipping solid angle check for 0 points using Qhull\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220709-17:13:18,262 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 14.20/14.40, Free processors: 0/1.\n",
      "                     Currently running:\n",
      "                       * eeg_pipeline.eeg_source_imaging_stage.mne_createfwd\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done   2 out of   4 | elapsed:    8.9s remaining:    8.9s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    9.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    9.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Skipping interior check for 1721 sources that fit inside a sphere of radius   53.7 mm\n",
      "    Skipping solid angle check for 0 points using Qhull\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   4 | elapsed:    0.3s remaining:    0.3s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Setting up for EEG...\n",
      "Computing EEG at 8196 source locations (free orientations)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   2 out of   4 | elapsed:    1.1s remaining:    1.1s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    1.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done   4 out of   4 | elapsed:    1.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished.\n",
      "    Write a source space...\n",
      "    [done]\n",
      "    Write a source space...\n",
      "    [done]\n",
      "    2 source spaces written\n",
      "220709-17:13:30,321 nipype.workflow INFO:\n",
      "\t [Node] Finished \"mne_createfwd\", elapsed time 13.943436s.\n",
      "220709-17:13:32,276 nipype.workflow INFO:\n",
      "\t [Job 5] Completed (eeg_pipeline.eeg_source_imaging_stage.mne_createfwd).\n",
      "220709-17:13:32,282 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:13:32,370 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_source_imaging_stage.mne_invsol\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_invsol\".\n",
      "220709-17:13:32,379 nipype.workflow INFO:\n",
      "\t [Node] Executing \"mne_invsol\" <cmtklib.interfaces.mne.MNEInverseSolutionROI>\n",
      "Reading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preprocessing_stage/eeglab2fif/epo.fif ...\n",
      "    Read a total of 1 projection items:\n",
      "        Average EEG reference (1 x 128)  idle\n",
      "    Found the data of interest:\n",
      "        t =    -200.00 ...     500.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "588 matching events found\n",
      "No baseline correction applied\n",
      "Created an SSP operator (subspace dimension = 1)\n",
      "1 projection items activated\n",
      "Reading forward solution from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_createfwd/fwd.fif...\n",
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    2 source spaces read\n",
      "    Desired named matrix (kind = 3523) not available\n",
      "    Read EEG forward solution (8196 sources, 128 channels, free orientations)\n",
      "    Source spaces transformed to the forward solution coordinate frame\n",
      "    128 x 128 full covariance (kind = 1) found.\n",
      "    Read a total of 1 projection items:\n",
      "        Average EEG reference (1 x 128) active\n",
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    2 source spaces read\n",
      "Computing inverse operator with 128 channels.\n",
      "    128 out of 128 channels remain after picking\n",
      "Selected 128 channels\n",
      "Whitening the forward solution.\n",
      "    Created an SSP operator (subspace dimension = 1)\n",
      "Computing rank from covariance with rank=None\n",
      "    Using tolerance 1.2e-14 (2.2e-16 eps * 128 dim * 0.43  max singular value)\n",
      "    Estimated rank (eeg): 127\n",
      "    EEG: rank 127 computed from 128 data channels with 1 projector\n",
      "    Setting small EEG eigenvalues to zero (without PCA)\n",
      "Creating the source covariance matrix\n",
      "Adjusting source covariance matrix.\n",
      "Computing SVD of whitened and weighted lead field matrix.\n",
      "220709-17:13:34,277 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 14.20/14.40, Free processors: 0/1.\n",
      "                     Currently running:\n",
      "                       * eeg_pipeline.eeg_source_imaging_stage.mne_invsol\n",
      "    largest singular value = 6.48933\n",
      "    scaling factor to adjust the trace = 4.66048e+24 (nchan = 128 nzero = 1)\n",
      "Write inverse operator decomposition in /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_source_imaging_stage/mne_invsol/inv.fif...\n",
      "    Write a source space...\n",
      "    [done]\n",
      "    Write a source space...\n",
      "    [done]\n",
      "    2 source spaces written\n",
      "    Writing inverse operator info...\n",
      "    Writing noise covariance matrix.\n",
      "    Writing source covariance matrix.\n",
      "    Writing orientation priors.\n",
      "    [done]\n",
      "Preparing the inverse operator for use...\n",
      "    Scaled noise and source covariance from nave = 1 to nave = 588\n",
      "    Created the regularized inverter\n",
      "    Created an SSP operator (subspace dimension = 1)\n",
      "    Created the whitener using a noise covariance matrix with rank 127 (1 small eigenvalues omitted)\n",
      "    Computing noise-normalization factors (sLORETA)...\n",
      "[done]\n",
      "Picked 128 channels from the data\n",
      "Computing inverse...\n",
      "    Eigenleads need to be weighted ...\n",
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      "[done]\n",
      "Reading labels from parcellation...\n",
      "   read 35 labels from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/label/lh.lausanne2018.scale1.annot\n",
      "   read 35 labels from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/label/rh.lausanne2018.scale1.annot\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
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     ]
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      "Extracting time courses for 70 labels (mode: pca_flip)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
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      "Extracting time courses for 70 labels (mode: pca_flip)\n",
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      "Extracting time courses for 70 labels (mode: pca_flip)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
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      "Extracting time courses for 70 labels (mode: pca_flip)\n",
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      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "Extracting time courses for 70 labels (mode: pca_flip)\n",
      "220709-17:16:19,251 nipype.workflow INFO:\n",
      "\t [Node] Finished \"mne_invsol\", elapsed time 166.864983s.\n",
      "220709-17:16:20,461 nipype.workflow INFO:\n",
      "\t [Job 6] Completed (eeg_pipeline.eeg_source_imaging_stage.mne_invsol).\n",
      "220709-17:16:20,473 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:16:20,889 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_connectome_stage.eeg_compute_matrice\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_connectome_stage/eeg_compute_matrice\".\n",
      "220709-17:16:20,901 nipype.workflow INFO:\n",
      "\t [Node] Executing \"eeg_compute_matrice\" <cmtklib.interfaces.mne.MNESpectralConnectivity>\n",
      "Reading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preprocessing_stage/eeglab2fif/epo.fif ...\n",
      "    Read a total of 1 projection items:\n",
      "        Average EEG reference (1 x 128)  idle\n",
      "    Found the data of interest:\n",
      "        t =    -200.00 ...     500.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "588 matching events found\n",
      "No baseline correction applied\n",
      "Created an SSP operator (subspace dimension = 1)\n",
      "1 projection items activated\n",
      "220709-17:16:22,464 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 14.20/14.40, Free processors: 0/1.\n",
      "                     Currently running:\n",
      "                       * eeg_pipeline.eeg_connectome_stage.eeg_compute_matrice\n",
      "Save /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_connectome_stage/eeg_compute_matrice/conndata-network_connectivity.tsv...\n",
      "Save /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_connectome_stage/eeg_compute_matrice/conndata-network_connectivity.gpickle...\n",
      "Save /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_connectome_stage/eeg_compute_matrice/conndata-network_connectivity.mat...\n",
      "Save /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_connectome_stage/eeg_compute_matrice/conndata-network_connectivity.graphml...\n",
      "220709-17:17:14,972 nipype.workflow INFO:\n",
      "\t [Node] Finished \"eeg_compute_matrice\", elapsed time 54.067281s.\n",
      "220709-17:17:16,563 nipype.workflow INFO:\n",
      "\t [Job 7] Completed (eeg_pipeline.eeg_connectome_stage.eeg_compute_matrice).\n",
      "220709-17:17:16,570 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:17:16,685 nipype.workflow INFO:\n",
      "\t [Node] Setting-up \"eeg_pipeline.eeg_datasinker\" in \"/Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_datasinker\".\n",
      "220709-17:17:16,707 nipype.workflow INFO:\n",
      "\t [Node] Executing \"eeg_datasinker\" <nipype.interfaces.io.DataSink>\n",
      "220709-17:17:16,711 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/epo.fif -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_epo.fif\n",
      "220709-17:17:16,716 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/bem.fif -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_bem.fif\n",
      "220709-17:17:16,720 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/noisecov.fif -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_noisecov.fif\n",
      "220709-17:17:16,724 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/src.fif -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_src.fif\n",
      "220709-17:17:16,727 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/fwd.fif -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_fwd.fif\n",
      "220709-17:17:16,731 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/inv.fif -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_inv.fif\n",
      "220709-17:17:16,734 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/timeseries.pickle -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_atlas-L2018_res-scale1_timeseries.pickle\n",
      "220709-17:17:16,736 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/conndata-network_connectivity.tsv -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_atlas-L2018_res-scale1_conndata-network_connectivity.tsv\n",
      "220709-17:17:16,739 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/conndata-network_connectivity.gpickle -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_atlas-L2018_res-scale1_conndata-network_connectivity.gpickle\n",
      "220709-17:17:16,742 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/conndata-network_connectivity.mat -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_atlas-L2018_res-scale1_conndata-network_connectivity.mat\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220709-17:17:16,745 nipype.interface INFO:\n",
      "\t sub: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/conndata-network_connectivity.graphml -> /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_atlas-L2018_res-scale1_conndata-network_connectivity.graphml\n",
      "220709-17:17:16,749 nipype.workflow INFO:\n",
      "\t [Node] Finished \"eeg_datasinker\", elapsed time 0.037833s.\n",
      "220709-17:17:18,568 nipype.workflow INFO:\n",
      "\t [Job 8] Completed (eeg_pipeline.eeg_datasinker).\n",
      "220709-17:17:18,576 nipype.workflow INFO:\n",
      "\t [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 14.40/14.40, Free processors: 1/1.\n",
      "220709-17:17:21,115 nipype.interface INFO:\n",
      "\t **** Processing finished ****\n",
      "CPU times: user 16.5 s, sys: 1.98 s, total: 18.4 s\n",
      "Wall time: 15min 58s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:320: UserWarning: resource_tracker: There appear to be 6 leaked folder objects to clean up at shutdown\n",
      "  (len(rtype_registry), rtype))\n",
      "/Applications/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:333: UserWarning: resource_tracker: /var/folders/vy/0bw_1jvj54n8lvcgvdrtqb0c0000gn/T/joblib_memmapping_folder_47551_379a43977f814190bfe6f03e92cfdae6_3fedd64ce92b49b9a6481229dccca45d: FileNotFoundError(2, 'No such file or directory')\n",
      "  warnings.warn('resource_tracker: %s: %r' % (name, e))\n",
      "/Applications/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:333: UserWarning: resource_tracker: /var/folders/vy/0bw_1jvj54n8lvcgvdrtqb0c0000gn/T/joblib_memmapping_folder_47551_379a43977f814190bfe6f03e92cfdae6_ae6b9e5318df4c718513b5ce780ef199: FileNotFoundError(2, 'No such file or directory')\n",
      "  warnings.warn('resource_tracker: %s: %r' % (name, e))\n",
      "/Applications/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:333: UserWarning: resource_tracker: /var/folders/vy/0bw_1jvj54n8lvcgvdrtqb0c0000gn/T/joblib_memmapping_folder_47551_3d28f25931e34ead98c3102ee1bbe4be_e207a49f2faf4ad38c389a170ccc9af2: FileNotFoundError(2, 'No such file or directory')\n",
      "  warnings.warn('resource_tracker: %s: %r' % (name, e))\n",
      "/Applications/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:333: UserWarning: resource_tracker: /var/folders/vy/0bw_1jvj54n8lvcgvdrtqb0c0000gn/T/joblib_memmapping_folder_47551_379a43977f814190bfe6f03e92cfdae6_5d99e23deff54a888a0efa2ed85d7b6f: FileNotFoundError(2, 'No such file or directory')\n",
      "  warnings.warn('resource_tracker: %s: %r' % (name, e))\n",
      "/Applications/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:333: UserWarning: resource_tracker: /var/folders/vy/0bw_1jvj54n8lvcgvdrtqb0c0000gn/T/joblib_memmapping_folder_47551_ad22804ddb624e55b2ee83b946e96936_428a96c1454840a28c73e60ad46a69c8: FileNotFoundError(2, 'No such file or directory')\n",
      "  warnings.warn('resource_tracker: %s: %r' % (name, e))\n",
      "/Applications/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:333: UserWarning: resource_tracker: /var/folders/vy/0bw_1jvj54n8lvcgvdrtqb0c0000gn/T/joblib_memmapping_folder_47551_379a43977f814190bfe6f03e92cfdae6_d7a66cd336f341f8909b02f329faff21: FileNotFoundError(2, 'No such file or directory')\n",
      "  warnings.warn('resource_tracker: %s: %r' % (name, e))\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from cmtklib import config\n",
    "\n",
    "# IF on MacOSX, add /usr/sbin to the $PATH\n",
    "# which contains sysctl\n",
    "# Otherwise, Nipype raises an \"/bin/sh: sysctl: command not found\" error\n",
    "# when trying to get the system memory\n",
    "if \"darwin\" in sys.platform:\n",
    "    os.environ[\"PATH\"] = f'/usr/sbin/:{os.environ[\"PATH\"]}'\n",
    "# Note that \"sysctl\" can be located in a different place\n",
    "# than \"/usr/sbin\".\n",
    "# To know which path has to be added, you can run\n",
    "# `locate sysctl`\n",
    "\n",
    "# Set the path to the anatomical pipeline configurration file\n",
    "eeg_pipeline_config = 'ref_mne_eeg_config.json'\n",
    "project.eeg_config_file = os.path.abspath(eeg_pipeline_config)\n",
    "\n",
    "if anat_valid_outputs:\n",
    "    # Initialize the EEG pipeline reading the configuration file and\n",
    "    # check input validity\n",
    "    eeg_valid_inputs, eeg_pipeline = cmp.project.init_eeg_project(\n",
    "        project, False\n",
    "    )\n",
    "    if eeg_pipeline is not None:\n",
    "        eeg_pipeline.parcellation_scheme = anat_pipeline.parcellation_scheme\n",
    "        eeg_pipeline.atlas_info = anat_pipeline.atlas_info\n",
    "        eeg_pipeline.stages['EEGPreprocessing'].config.task_label = 'faces'\n",
    "\n",
    "        if eeg_valid_inputs:\n",
    "            print(\">> Process EEG pipeline\")\n",
    "            eeg_pipeline.process()\n",
    "        else:\n",
    "            print(\"  .. ERROR: Invalid inputs\")\n",
    "            exit_code = 1\n",
    "else:\n",
    "    print_error(f'  .. ERROR: Invalid anatomical outputs for eeg pipeline')\n",
    "    print_error(f'{msg}')\n",
    "    exit_code = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58970fdc",
   "metadata": {},
   "source": [
    "## A closer look at the EEG pipeline outputs\n",
    "\n",
    "Let's have a closer look at the outputs that the EEG pipeline produces in the `derivatives/cmp-v3.1.0` derivatives directory.\n",
    "\n",
    "First of all, connectomemapper works in such a way that the pipeline is first assembled and only afterwards, it is executed. During the assembly stage, input and output variables are connected and CMP3 produces a graph that visualizes this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b969143",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.SVG object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "path_to_svg = os.path.join(\n",
    "    output_dir, __nipype_directory__, participant_label, 'eeg_pipeline','graph.svg'\n",
    ")\n",
    "display(SVG(filename=path_to_svg))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7ffaad",
   "metadata": {},
   "source": [
    "You can see three blue boxes that represent the different stages of the pipeline flow:\n",
    "\n",
    "    * EEG preprocessing stage\n",
    "    * EEG source imaging stage\n",
    "    * EEG connectome stage\n",
    "\n",
    "Each of the stages, again, has an input and and output node, as well as several nodes representing processing steps. Each processing step has its own \"interface\" which you can find in ``cmtklib/interfaces`` (\"mne\" in parentheses indicates that they are defined in the file mne.py).\n",
    "\n",
    "`eeg_datasource` is the input BIDSDataGrabber node and `eeg_datasinker` is the output DataSinker node. `datasource` takes care of querying and injecting the input files in the different stages of the EEG pipeline. `eeg_sinker` is taking care of collecting, moving, and renaming all the files produced by the different stages to the `derivatives/cmp-v3.1.0` directory.\n",
    "\n",
    "In the following, we will go over the interfaces and show what output they produce. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19804780",
   "metadata": {},
   "source": [
    "### EEG preprocessing stage\n",
    "\n",
    "The preprocessing stage consists of converting EEGLab `.set` EEG files to MNE Epochs in `.fif` format, the format used in the rest of the pipeline by calling, if necessary the following interface: \n",
    "\n",
    "- `EEGLAB2fif`: Read EEGLab data and converts them to MNE format (.fif file extension).\n",
    "\n",
    "The information given by the config file regarding this stage is as follows: \n",
    "\n",
    "```json\n",
    "[...]\n",
    "    \"eeg_preprocessing_stage\": {\n",
    "        \"task_label\": \"faces\",\n",
    "        \"eeg_ts_file.extension\": \"set\",\n",
    "        \"eeg_ts_file.toolbox_derivatives_dir\": \"eeglab-v14.1.1\",\n",
    "        \"eeg_ts_file.datatype\": \"eeg\",\n",
    "        \"eeg_ts_file.suffix\": \"eeg\",\n",
    "        \"eeg_ts_file.desc\": \"preproc\",\n",
    "        \"eeg_ts_file.task\": \"faces\",\n",
    "        \"events_file.datatype\": \"eeg\",\n",
    "        \"events_file.suffix\": \"events\",\n",
    "        \"events_file.extension\": \"tsv\",\n",
    "        \"events_file.task\": \"faces\",\n",
    "        \"electrodes_file_fmt\": \"Cartool\",\n",
    "        \"cartool_electrodes_file.toolbox_derivatives_dir\": \"cartool-v3.80\",\n",
    "        \"cartool_electrodes_file.datatype\": \"eeg\",\n",
    "        \"cartool_electrodes_file.suffix\": \"eeg\",\n",
    "        \"cartool_electrodes_file.extension\": \"xyz\",\n",
    "        \"t_min\": -0.2,\n",
    "        \"t_max\": 0.5\n",
    "    },\n",
    "[...]\n",
    "```\n",
    "\n",
    "#### EEGLAB2fif\n",
    "\n",
    "If your data are not already in MNE format (`.fif` file extension), they have to be read and re-saved. The eeglab2fif interface does this for EEGLAB-format data (`.set` file extension).\n",
    "\n",
    "The interface produces a file named `sub-01_epo.fif` in the `derivatives/cmp-v3.0.3` folder.\n",
    "\n",
    "Critically, the saved epochs contain a montage, i.e. the sensor locations which have to be supplied in a file names `sub-01.xyz` inside the subject's EEGLAB derivatives folder (`derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01.xyz`). *Not sure it still applied :)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "043b38d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting parameters from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set...\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "588 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Ready.\n",
      "Applying baseline correction (mode: mean)\n",
      "Reading /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_epo.fif ...\n",
      "    Read a total of 1 projection items:\n",
      "        Average EEG reference (1 x 128)  idle\n",
      "    Found the data of interest:\n",
      "        t =    -200.00 ...     500.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "588 matching events found\n",
      "No baseline correction applied\n",
      "Created an SSP operator (subspace dimension = 1)\n",
      "1 projection items activated\n"
     ]
    }
   ],
   "source": [
    "# Let's have a look at the EEG data\n",
    "with warnings.catch_warnings(): # suppress some irrelevant warnings coming from mne.read_epochs_eeglab()\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    epochs_eeglab = mne.read_epochs_eeglab(\n",
    "        os.path.join(output_dir, __eeglab_directory__,\n",
    "                     participant_label, 'eeg',\n",
    "                     participant_label + f'_task-{task_label}_desc-preproc_eeg.set')\n",
    "    ) # sub-01_FACES_250HZ_prepd.set\n",
    "\n",
    "# eeglab2fif removes a baseline and crops the epochs according to parameters start_t and end_t in config file\n",
    "start_t = -0.2\n",
    "end_t = 0.6\n",
    "epochs_eeglab.apply_baseline((start_t, 0))\n",
    "epochs_eeglab.crop(tmin=start_t, tmax=end_t)\n",
    "evoked_eeglab = epochs_eeglab.average().pick('eeg')\n",
    "\n",
    "# compare to what eeglab2fif saved\n",
    "epochs_mne = mne.read_epochs(\n",
    "    os.path.join(output_dir, __cmp_directory__,\n",
    "                 participant_label, 'eeg',\n",
    "                 participant_label + f'_task-{task_label}_epo.fif'))\n",
    "evoked_mne = epochs_mne.average().pick('eeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b05ee16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot and convince yourself it's the same\n",
    "%matplotlib inline\n",
    "fig = plt.figure()\n",
    "plt.rcParams['figure.figsize'] = (15, 10)\n",
    "\n",
    "_ = evoked_mne.plot(time_unit='s')\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.rcParams['figure.figsize'] = (15, 10)\n",
    "\n",
    "_ = evoked_eeglab.plot(time_unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b833237",
   "metadata": {},
   "source": [
    "### EEG source imaging stage\n",
    "\n",
    "This stage takes your data in fif format from the \"Preprocessing Stage\", the parcellation, and the previously generated electrode transform file as inputs. With the aim to compute inverse solutions and extract ROI time courses with MNE, its workflow consists of five processing interfaces, : \n",
    "\n",
    "- `CreateBEM`: Create the boundary element method.\n",
    "- `CreateSrc`: Create the dipole locations along the surface of the brain.\n",
    "- `CreateFwd`: Create the forward solution (leadfield) from the BEM and the source space.\n",
    "- `CreateCov`: Create the noise covariance matrix from the data.\n",
    "- `MNEInverseSolutionROI`: Create the actual inverse operator and applies it, resulting in ROI-time courses.\n",
    "\n",
    "The following possible EEG source imaging algorithms can be used for computing the inverse solutions: \"sLORETA\", \"eLORETA\", \"MNE\", and \"dSPM\". The configuration file of this tutorial is set to use \"sLORETA\".\n",
    "\n",
    "The information given by the config file regarding this stage is as follows: \n",
    "\n",
    "```json\n",
    "[...]\n",
    "    \"eeg_source_imaging_stage\": {\n",
    "        \"esi_tool\": \"MNE\",\n",
    "        \"mne_apply_electrode_transform\": true,\n",
    "        \"mne_electrode_transform_file.toolbox_derivatives_dir\": \"cmp-v3.1.0\",\n",
    "        \"mne_electrode_transform_file.datatype\": \"eeg\",\n",
    "        \"mne_electrode_transform_file.suffix\": \"trans\",\n",
    "        \"mne_electrode_transform_file.extension\": \"fif\",\n",
    "        \"parcellation_scheme\": \"Lausanne2018\",\n",
    "        \"lausanne2018_parcellation_res\": \"scale1\",\n",
    "        \"mne_esi_method\": \"sLORETA\",\n",
    "        \"mne_esi_method_snr\": 3.0\n",
    "    },\n",
    "[...]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff050913",
   "metadata": {},
   "source": [
    "#### CreateBEM\n",
    "\n",
    "The BEM (boundary element model) is the head model we use, in our case, it is based on the individual's structural MRI and, again, related freesurfer derivatives. Its creation consists of two steps:\n",
    "\n",
    "1. The necessary surfaces (brain, inner skull, outer skull, and outer skin) are extracted using ``mne.bem.make_watershed_bem()``. The surfaces are saved in the subject's freesurfer-directory in a new folder `bem/` watershed.\n",
    "\n",
    "2. The model itself is created using ``mne.make_bem_model()`` and ``mne.make_bem_solution()``. In this step, the surfaces and the tissue conductivities between the surfaces are used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08b4719b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    Reading a source space...\n",
      "    Computing patch statistics...\n",
      "    Patch information added...\n",
      "    Distance information added...\n",
      "    [done]\n",
      "    2 source spaces read\n",
      "Using surface: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/bem/inner_skull.surf\n",
      "Using surface: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/bem/outer_skull.surf\n",
      "Using surface: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/bem/outer_skin.surf\n"
     ]
    }
   ],
   "source": [
    "# Let's visualize the BEM surfaces and source space\n",
    "src = mne.read_source_spaces(\n",
    "    os.path.join(\n",
    "        output_dir,__cmp_directory__,\n",
    "        participant_label, 'eeg', participant_label + f'_task-{task_label}_src.fif'))\n",
    "# plot will appear in separate window\n",
    "%matplotlib qt \n",
    "# lines are the surfaces, pink dots are the sources (dipoles)\n",
    "_=mne.viz.plot_bem(\n",
    "    subject=participant_label,\n",
    "    subjects_dir=project.freesurfer_subjects_dir,\n",
    "    brain_surfaces='white',\n",
    "    src=src,\n",
    "    orientation='sagittal'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77dcacca",
   "metadata": {},
   "source": [
    "#### CreateSrc\n",
    "\n",
    "MNE is able to create volume- and surface-based source spaces, but in our pipeline, we use surface-based only. \n",
    "\n",
    "In order to do this, MNE takes advantage of the Freesurfer-created outputs in the `derivatives/freesurfer-7.1.1` derivatives directory. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1d4d63",
   "metadata": {},
   "source": [
    "#### CreateFwd\n",
    "\n",
    "MNE first computes a forward solution that describes how electrical currents propagate from the sources created earlier (via createsrc) through the tissues of the head modelled by the BEM (created via createbem) to the electrodes. Thus, the electrode positions have to be known and be aligned to the head model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5a081bdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using pyvistaqt 3d backend.\n",
      "\n",
      "Using outer_skin.surf for head surface.\n",
      "Channel types::\teeg: 128\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mne.viz.backends._pyvista._Figure at 0x7fc713dd9710>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the alignment between MRI and electrode positions.\n",
    "trans = mne.read_trans(\n",
    "    os.path.join(\n",
    "        output_dir, __cmp_directory__,\n",
    "        participant_label, 'eeg', participant_label + '_trans.fif'\n",
    "    )\n",
    ")\n",
    "mne.viz.plot_alignment(\n",
    "    epochs_mne.info,\n",
    "    trans=trans,\n",
    "    subject=participant_label,\n",
    "    subjects_dir=project.freesurfer_subjects_dir,\n",
    "    dig=False,\n",
    "    surfaces=dict(head=0.95),\n",
    "    coord_frame='mri')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ca5e68",
   "metadata": {},
   "source": [
    "#### CreateCov\n",
    "\n",
    "MNE uses an estimate of signal to noise ratio in its creation of the inverse solution. For that, it considers the pre-stimulus period of the EEG recordings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9922942d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    128 x 128 full covariance (kind = 1) found.\n",
      "    Read a total of 1 projection items:\n",
      "        Average EEG reference (1 x 128) active\n",
      "Computing rank from covariance with rank=None\n",
      "    Using tolerance 1.2e-14 (2.2e-16 eps * 128 dim * 0.43  max singular value)\n",
      "    Estimated rank (eeg): 127\n",
      "    EEG: rank 127 computed from 128 data channels with 0 projectors\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 273.6x266.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 273.6x266.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's have a look at the noise covariance.\n",
    "%matplotlib inline\n",
    "noise_cov = mne.read_cov(\n",
    "    os.path.join(\n",
    "        output_dir, __cmp_directory__,\n",
    "        participant_label, 'eeg', participant_label + f'_task-{task_label}_noisecov.fif'\n",
    "    )\n",
    ")\n",
    "fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, epochs_mne.info)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af42302e",
   "metadata": {},
   "source": [
    "#### MNEInverseSolutionROI\n",
    "\n",
    "Now, everything comes together to create the inverse operator, which is then applied to the EEG data to create source time courses. In the last step, the source time courses are converted to ROI-time courses according to the selected parcellation. \n",
    "\n",
    "The outputs that are necessary for this step to work were created in the previous processing steps, namely: \n",
    "\n",
    "- the EEG epochs in `.fif` format\n",
    "- the electrode montage\n",
    "- the head model\n",
    "- the source point locations\n",
    "- the forward operator\n",
    "- the noise covariance\n",
    "\n",
    "First, the inverse operator is created using ``mne.minimum_norm.make_inverse_operator()``.\n",
    "We use the options `loose=1`, `depth=None`, and `fixed=False` to obtain full 3-dimensional dipoles whose orientation is not fixed or constrained to be (somewhat) orthogonal to surface; and we are not applying any depth weighting. The solution is finally written to a file `sub-01_task-faces_inv.fif` in the same directory as the other outputs (`derivatives/cmp-v3.1.0/sub-01/eeg`). \n",
    "\n",
    "In a subsequent step in the same interface, this inverse operator is then applied to the epochs (not the evoked time course averaged over trials) using mne.minimum_norm.apply_inverse_epochs. \n",
    "\n",
    "The final step performed by this interface and by the EEG pipeline is to use mne.extract_label_time_course to create ROI-time courses according to ``mne.read_labels_from_annot()``. As given in the config file, we use \"lausanne2008\" scale 1, which is the Desikan-atlas. The time courses and the ROI-names are stored in ``sub-01_task-faces_atlas-L2018_res-scale1_timeseries.pickle`` in pickle format.\n",
    "\n",
    "Let's have a look at the time courses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f02381d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the generated ROI time series file\n",
    "roi_ts_fname = participant_label + f'_task-{task_label}_atlas-L2018_res-scale1_timeseries.pickle'\n",
    "roi_ts_file = os.path.join(\n",
    "    output_dir, __cmp_directory__,\n",
    "    participant_label, 'eeg', roi_ts_fname\n",
    ")\n",
    "with open(roi_ts_file, 'rb') as f:\n",
    "    rtc_epo = pickle.load(f)\n",
    "    # For some reason, MNE writes label time courses as lists. convert to numpy array\n",
    "    rtc_epo['data'] = np.array(rtc_epo['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8e064921",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort labels to make the time courses look nicer\n",
    "N = len(rtc_epo['labels']) - 2  # two \"unknown\" regions - do not plot\n",
    "sorting = list(np.arange(0, N, 2)) + list(np.arange(1, N, 2))  # left and right always alternating\n",
    "# List of ROI names\n",
    "labels_list_left = [i.name for i in rtc_epo['labels'][0::2] if i.name != 'unknown -lh']\n",
    "labels_list_right = [i.name for i in rtc_epo['labels'][1::2] if i.name != 'unknown -rh']\n",
    "labels_list = labels_list_left + labels_list_right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c497706c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "%matplotlib inline\n",
    "to_plot = np.mean(rtc_epo['data'][:, :-2, :], axis=0)\n",
    "vminmax = np.max(abs(to_plot))\n",
    "plt.rcParams['figure.figsize'] = (15, 10)\n",
    "plt.imshow(\n",
    "    to_plot[sorting, :],\n",
    "    aspect='auto',\n",
    "    extent=[-200, 600, 0, 67],\n",
    "    interpolation='None',\n",
    "    vmin=-vminmax,\n",
    "    vmax=vminmax,\n",
    "    cmap='PiYG'\n",
    ");\n",
    "plt.xlabel('ms')\n",
    "plt.ylabel('ROIs')\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('source activity (a.u.)')\n",
    "locs = np.arange(0, N)\n",
    "_ = plt.yticks(locs, labels_list[-1::-1] )  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2624bff8",
   "metadata": {},
   "source": [
    "We can see that some of the time courses are \"flipped\" (have the opposite sign of the others). We will not address this problem here, but this is because of the step where dipole time courses are summarized for each brain region, using PCA. The direction of the resulting vector is not uniquely defined.\n",
    "\n",
    "This leads us to the last stage of the pipeline, the \"Connectome Stage\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb5f6aa",
   "metadata": {},
   "source": [
    "### EEG connectome stage\n",
    "\n",
    "This stage aims to map the connectome from the extracted ROI time series and consists of one processing step: \n",
    "\n",
    "- `MNESpectralConnectivity`: Compute frequency- and time-frequency-domain connectivity measures and save the connectom files in different format.\n",
    "\n",
    "The information given by the config file regarding this stage is as follows: \n",
    "\n",
    "```json\n",
    "[...]\n",
    "    \"eeg_connectome_stage\": {\n",
    "        \"connectivity_metrics\": [\n",
    "            \"coh\",\n",
    "            \"cohy\",\n",
    "            \"imcoh\",\n",
    "            \"plv\",\n",
    "            \"ciplv\",\n",
    "            \"ppc\",\n",
    "            \"pli\",\n",
    "            \"wpli\",\n",
    "            \"wpli2_debiased\"\n",
    "        ],\n",
    "        \"output_types\": [\n",
    "            \"tsv\"\n",
    "            \"gPickle\",\n",
    "            \"mat\",\n",
    "            \"graphml\"\n",
    "        ]\n",
    "    },\n",
    "[...]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5107dce6",
   "metadata": {},
   "source": [
    "#### MNESpectralConnectivity\n",
    "\n",
    "CMP3 uses [MNE-Connectivity](https://mne.tools/mne-connectivity) to compute the functional connectivity matrices. Results can be saved in the same formats (`['tsv', 'gPickle', 'mat', 'graphml']`) as the diffusion MRI and resting-state fMRI pipelines.\n",
    "\n",
    "Keep in mind that we only plot a single subject's connectivity here, so it is not surprising if you do not see exactly what you would expect."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a62c20f-c105-4ea9-bf88-d8aab201719d",
   "metadata": {},
   "source": [
    "We can load the matrices in network format, by reading the gpickle files using Networkx:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ce43fd9-3dff-45f1-a81e-0b822039a066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load dataset_description for: /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0\n",
      "Load wpli2_debiased connectivity matrix from /Users/sebastientourbier/Documents/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.1.0/sub-01/eeg/sub-01_task-faces_atlas-L2018_res-scale1_conndata-network_connectivity.gpickle\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.00300068, -0.00054407, ...,  0.00141492,\n",
       "        -0.00075917, -0.00026943],\n",
       "       [ 0.00300068,  0.        ,  0.00172582, ...,  0.00125406,\n",
       "        -0.00034805, -0.00099333],\n",
       "       [-0.00054407,  0.00172582,  0.        , ..., -0.0008333 ,\n",
       "         0.00116659,  0.00129021],\n",
       "       ...,\n",
       "       [ 0.00141492,  0.00125406, -0.0008333 , ...,  0.        ,\n",
       "        -0.00041457,  0.00171886],\n",
       "       [-0.00075917, -0.00034805,  0.00116659, ..., -0.00041457,\n",
       "         0.        ,  0.00133674],\n",
       "       [-0.00026943, -0.00099333,  0.00129021, ...,  0.00171886,\n",
       "         0.00133674,  0.        ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index the new CMP3 derivatives including the connectome files\n",
    "# in the BIDSLayout representation\n",
    "bids_layout.add_derivatives(os.path.join(project.base_directory, \"derivatives\", \"cmp-v3.1.0\"))\n",
    "\n",
    "# Query the generated connectome gpickle file\n",
    "bids_query = {\n",
    "    \"subject\": participant_label.split('-')[-1],  # Keep the label only, e.g. \"01\"\n",
    "    \"datatype\": 'eeg',\n",
    "    \"atlas\": 'L2018',\n",
    "    \"res\": 'scale1',\n",
    "    \"suffix\": 'connectivity',\n",
    "    \"extension\": 'gpickle',\n",
    "    \"return_type\": 'filename'\n",
    "}\n",
    "cmat_file = bids_layout.get(**bids_query)[0]  # BIDSLayout always return a list\n",
    "\n",
    "# Load wpli2_debiased connectivity matrix from the connectome gpickle file\n",
    "weight = \"wpli2_debiased\"\n",
    "print(f'Load {weight} connectivity matrix from {cmat_file}')\n",
    "G = nx.read_gpickle(cmat_file)  \n",
    "A_wpli2_debiased = nx.to_numpy_array(G, weight=weight)\n",
    "A_wpli2_debiased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de3a495-4579-46f0-a0e2-a8e098462f13",
   "metadata": {},
   "source": [
    "Then, we can load and order the name of the labels from the dictionary storing the ROI timeseries results, and visualize the connectivity matrix in a pretty circular layout with [MNE-Connectivity](https://mne.tools/mne-connectivity) `viz.plot_connectivity_circle()` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "68fbc204",
   "metadata": {
    "nbsphinx-thumbnail": {
     "output-index": 0,
     "tooltip": "Get a first look at the new EEG pipeline of CMP3"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.77 s, sys: 47.8 ms, total: 8.82 s\n",
      "Wall time: 8.41 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x576 with 2 Axes>, <PolarAxesSubplot:>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "label_names = [label.name for label in rtc_epo['labels']]\n",
    "\n",
    "lh_labels = [name for name in label_names if name.endswith('lh')]\n",
    "\n",
    "# Get the y-location of the label\n",
    "label_ypos = list()\n",
    "for name in lh_labels:\n",
    "    idx = label_names.index(name)\n",
    "    ypos = np.mean(rtc_epo['labels'][idx].pos[:, 1])\n",
    "    label_ypos.append(ypos)\n",
    "\n",
    "# Reorder the labels based on their location\n",
    "lh_labels = [label for (yp, label) in sorted(zip(label_ypos, lh_labels))]\n",
    "\n",
    "# For the right hemi\n",
    "rh_labels = [label[:-2] + 'rh' for label in lh_labels]\n",
    "\n",
    "# Save the plot order and create a circular layout\n",
    "node_order = list()\n",
    "node_order.extend(lh_labels[::-1])  # reverse the order\n",
    "node_order.extend(rh_labels)\n",
    "\n",
    "node_angles = mnec.viz.circular_layout(label_names, node_order, start_pos=90,\n",
    "                              group_boundaries=[0, len(label_names) / 2])\n",
    "\n",
    "# Plot the graph using node colors from the FreeSurfer parcellation. We only\n",
    "# show the 300 strongest connections.\n",
    "# plot will appear in separate window\n",
    "%matplotlib inline \n",
    "mnec.viz.plot_connectivity_circle(A_wpli2_debiased, label_names, n_lines=300,\n",
    "                         node_angles=node_angles, node_colors='r',\n",
    "                         title='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b6aba2",
   "metadata": {},
   "source": [
    "This concludes the tutorial 🧠!\n",
    "\n",
    "We hope you enjoy it and any feedback or suggestions to improve it are very welcome! Just please open a [new issue](https://github.com/connectomicslab/connectomemapper3/issues/new) on GitHub and share your thoughts with us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1668297",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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