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

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.

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.

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

Implementation of Correlation function and signal averaging method for detecting P300.

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