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.

Graphical interface for loading p300 frames in an ATMega32 by serial port

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.

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.

For ear EEG testing and verifying

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 Correlation function and signal averaging method for detecting P300.

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

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

this is a simple artificial neural network that used to classify P300 without using any libraries

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）

An executable (.exe) from a python script for p300 segment extraction using different channels

Simple P300 speller

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

The Histogram of Gradient Orientations of EEG Signal Plots for BCI

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

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