  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.
