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This is the official implementation of the paper titled "Multi-Task Collaborative Network: Bridge the Supervised and Self-Supervised Learning for EEG Classification in RSVP tasks".
Implementation of CVPR 2020 paper "MMTM: Multimodal Transfer Module for CNN Fusion"
SZU-AdvTech-2022 / 381-EEG-ConvTransformer-for-Single-Trial-EEG-Based-Visual-Stimulus-Classification
Tengine is a lite, high performance, modular inference engine for embedded device
Implementation of “DreamDiffusion: Generating High-Quality Images from Brain EEG Signals”
[ICML 2021] Self-supervised pre-training of AI models on ECG data with CLOCS
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
Deep InfoMax (DIM), or "Learning Deep Representations by Mutual Information Estimation and Maximization"
Code and Data for "Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features"
About Open source code of paper: Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX
Official repository of EEG-Inception, a general-purpose and powerful deep convolutional neural network for EEG procesing
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
[ICLR 2024] M/EEG-based image decoding with contrastive learning. i. Propose a contrastive learning framework to align image and eeg. ii. Resolving brain activity for biological plausibility.
Python implementation of Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis
We propose a time-aware sampling network (TAS-Net) using deep reinforcement learning (DRL) for unsupervised emotion recognition, which is able to detect key emotion moments and disregard irrelevant…
A Library for Advanced Deep Time Series Models.