How Homogenizing the Channel-wise Magnitude Can Enhance EEG Classification Model?
This addresses data preprocessing challenges in EEG classification for brain-computer interface applications, though it appears to be an incremental improvement over existing methods.
The paper tackles the problem of data redundancy and dominant lead information in EEG classification by proposing a preprocessing method that homogenizes channel-wise magnitudes and applies edge detection. Their approach improves classification accuracy by 2-5% over current baselines without requiring large deep learning networks.
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.