This repository is created specifically for the EEG project seminar instructed by Dr. Alexa Ruel in summer semester of 2024 at Universität Hamburg

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

Workshop on standardized Brain-Computer Interface Framework

Project 300 : Student-Parent Portal by Nayem, Hajifa and Rayhan || CSE :: Metropolitan University

For ear EEG testing and verifying

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.

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.

Simple P300 speller

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.

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

Visually evoked potentials and P300 for the Attys (Windows/Linux)

Timeflux demos

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

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.

El presente repositorio contiene todos los archivos del prototipo del proyecto BCI basado en potenciales P300, para interacción con dispositvos del entorno y creación de entornos inteligentes.

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

Proyecto Final de Bioingeniería - Sistema para rehabilitación cognitiva basado en BCI por P300

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

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