[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
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Updated
Jul 10, 2019 - Jupyter Notebook
[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
A P300 online spelling mechanism for Emotiv headsets. It's completely written in Node.js, and the GUI is based on Electron and Vue.
EEG BCI Real-Time Applications: Contains real-time demonstrations of BCI applications
Timeflux demos
A tool for teaching P300 by showing the ongoing averaging process and classification
A basic demonstration how to use Python, MNE, and PyTorch to analyze EEG signal.
P300 Classification for EEG-based BCI system with Bayes LDA, SVM, LassoGLM and a Deep CNN methods
Workshop on standardized Brain-Computer Interface Framework
뉴로 마케팅을 활용한 광고 분석 프로그램
Framework for P300 wave detection and noise-based cyberattacks in Brain-Computer Interfaces - Enrique Tomás Martínez Beltrán
P300 Matrix for brain computer interfaces using html, CSS and JavaScript with mean error 1 millisecond
Extract the independant sources with Composite Approximate Joint Diagonalization (CAJD) for linear/bilinear data models
Welcome to the EEG Signal Analysis repository, focusing on the extraction of P300 signals using synchronous averaging techniques. This project aims to provide insights into the optimal number of repetitions required to reliably capture the P300 response, a crucial component in various applications such as BCIs and cognitive neuroscience research.
This example compares the classification performance of linear support vector machine (LinearSVC) on the Riemannian Transfer Learning method (RPA, Rodrigues et al., 2018) and the golden-standard subject-wise train-test cross-validation method using real P300 BCI data.
For my MSc dissertation, and in my role as a research data analyst, I am undertaking an analysis of electroencephalography data to investigate whether detection of the P300 neural signal can be utilised within an EEG Brain-Computer Interface to discern information from the minds of individuals, without the need for explicit communication.
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