Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

TL;DR

This paper explores using a trained CNN model to classify EEG motor imagery data, aiming to improve brain-computer interface systems by accurately identifying motor intentions from EEG signals.

Contribution

It introduces a CNN-based approach for classifying EEG motor imagery data and tests its effectiveness with both full and reduced data samples.

Findings

CNN successfully classifies motor imagery with high accuracy

Model performs well with smaller, sampled data

Potential for real-time brain-computer interface applications

Abstract

A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data.