How Homogenizing the Channel-wise Magnitude Can Enhance EEG Classification Model?

TL;DR

This paper introduces a novel EEG data preprocessing method called Inverted Channel-wise Magnitude Homogenization (ICWMH) that reduces redundancy and enhances classification accuracy by emphasizing significant data transitions.

Contribution

The paper presents a simple preprocessing approach that transforms EEG data into encoded images and applies edge detection to improve deep learning classification performance.

Findings

Improved EEG classification accuracy by 2-5%

Effective reduction of inter-channel biases

Enhanced feature emphasis with edge detection

Abstract

A significant challenge in the electroencephalogram EEG lies in the fact that current data representations involve multiple electrode signals, resulting in data redundancy and dominant lead information. However extensive research conducted on EEG classification focuses on designing model architectures without tackling the underlying issues. Otherwise, there has been a notable gap in addressing data preprocessing for EEG, leading to considerable computational overhead in Deep Learning (DL) processes. In light of these issues, we propose a simple yet effective approach for EEG data pre-processing. Our method first transforms the EEG data into an encoded image by an Inverted Channel-wise Magnitude Homogenization (ICWMH) to mitigate inter-channel biases. Next, we apply the edge detection technique on the EEG-encoded image combined with skip connection to emphasize the most significant transitions in the data while preserving structural and invariant information. By doing so, we can improve the EEG learning process efficiently without using a huge DL network. Our experimental evaluations reveal that we can significantly improve (i.e., from 2% to 5%) over current baselines.