An interpretable KAN-guided SNN architecture with channel-wise contribution tracking for EEG-based emotion recognition

Abstract

EEG-based emotion recognition is challenging due to high signal-to-noise ratio, subtle difference in emotional states and mainly inter-participant variability. Previous works have shown that Spiking Neural Networks (SNN) are suitable for capturing temporal EEG activity, however, their learned representations are difficult to interpret at a channel level. This study proposes a channel-wise KAN (Kolmogorov- Arnold Networks) guided SNN for interpretable emotion recognition using EEG data from the DER-VREEG dataset. The dataset classifies emotion from four distinct emotion classes: happy, bored, calm and scared using four channels that capture EEG data from TP9, TP10, AF7 and AF8 which are four distinct regions of the human brain. Unlike traditional SNN classifiers, our proposed model can estimate the contribution factor of each of the four EEG channels towards predicting a certain emotion class, which is crucial for neuroscientists to study how a specific region of the brain reacts to a specific emotion. The framework thus provides explainable EEG emotion recognition along with classification accuracy that is on par with existing related architectures.