A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network

Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features but face limitations like overfitting due to small datasets. To address this issue, we propose a lightweight CNN and assess its interpretability through the fully connected layer (FCL). Initially tested with two tasks (Task 1: open vs closed eyes, Task 2: interictal vs ictal stage), the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2, aligning with established neurophysiological characteristics. Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity (around 1.55 Hz) and hand movement, with consistent results across pericentral electroencephalogram (EEG) channels. Compared to recent research, our method stands out by delivering task-related spectral features through FCL, resulting in significantly fewer trainable parameters while maintaining comparable interpretability. This indicates its potential suitability for a wider array of EEG decoding scenarios.