[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.
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
P300 Classification for EEG-based BCI system with Bayes LDA, SVM, LassoGLM and a Deep CNN methods
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.
Implementation of Correlation function and signal averaging method for detecting P300.
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