Convolutional Neural Network and Adversarial Autoencoder in EEG images classification

TL;DR

This paper explores combining computer vision and neural networks to classify human brain activities from EEG topograms, advancing EEG data analysis in neuroscience.

Contribution

It introduces a novel approach using 2D EEG topograms and neural networks for classifying motor cortex activities.

Findings

Achieved effective classification of EEG data into different motor activities.

Demonstrated the feasibility of using computer vision techniques on EEG topograms.

Abstract

In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities.