Electroencephalogram Signal Processing with Independent Component Analysis and Cognitive Stress Classification using Convolutional Neural Networks

TL;DR

This paper presents a method combining ICA and cross-correlation for EEG signal denoising, followed by CNN-based classification of cognitive stress levels, improving signal clarity and stress detection accuracy.

Contribution

It introduces a novel ICA-based denoising approach that reduces artifact effects without significant data loss, enhancing EEG analysis for stress classification.

Findings

Effective removal of EOG artifacts with minimal EEG data loss

Increased SNR after denoising improves signal quality

CNN accurately classifies stress levels from cleaned EEG signals

Abstract

Electroencephalogram (EEG) is the recording which is the result due to the activity of bio-electrical signals that is acquired from electrodes placed on the scalp. In Electroencephalogram signal(EEG) recordings, the signals obtained are contaminated predominantly by the Electrooculogram(EOG) signal. Since this artifact has higher magnitude compared to EEG signals, these noise signals have to be removed in order to have a better understanding regarding the functioning of a human brain for applications such as medical diagnosis. This paper proposes an idea of using Independent Component Analysis(ICA) along with cross-correlation to de-noise EEG signal. This is done by selecting the component based on the cross-correlation coefficient with a threshold value and reducing its effect instead of zeroing it out completely, thus reducing the information loss. The results of the recorded data show that this algorithm can eliminate the EOG signal artifact with little loss in EEG data. The denoising is verified by an increase in SNR value and the decrease in cross-correlation coefficient value. The denoised signals are used to train an Artificial Neural Network(ANN) which would examine the features of the input EEG signal and predict the stress levels of the individual.