[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.

Python library for ESPEC North America chambers with P-300, SCP-220, Watlow F4, F4T controllers.

A basic demonstration how to use Python, MNE, and PyTorch to analyze EEG signal.

EEG BCI Real-Time Applications: Contains real-time demonstrations of BCI applications

Timeflux demos

P300 Classification for EEG-based BCI system with Bayes LDA, SVM, LassoGLM and a Deep CNN methods

A tool for teaching P300 by showing the ongoing averaging process and classification

Framework for P300 wave detection and noise-based cyberattacks in Brain-Computer Interfaces - Enrique Tomás Martínez Beltrán

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 P300 waveform detection algorithm proposed by the winners of BCI Competition III

Extract the independant sources with Composite Approximate Joint Diagonalization (CAJD) for linear/bilinear data models

Conduct research on the effect of different proportions of visual target and non-target stimulations on the brain, through data analysis on event related potential collected.

Assess ICA-denoising impact on the analysis of the event related potential P300, for an Autism Spectrum Disorder BCI dataset. Reject different numbers of Independent Components and compare them to common noise sources of EEG acquisitions.

P300 Matrix for brain computer interfaces using html, CSS and JavaScript with mean error 1 millisecond

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.

オドボール課題でP300を取得するためのプログラム（Programs to obtain P300 with Oddball Task）

Workshop on standardized Brain-Computer Interface Framework

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.