Interpretable EEG Emotion Classification via CNN Model and Gradient-Weighted Class Activation Mapping.

Background/

Objectives

Electroencephalography (EEG)-based emotion recognition plays an important role in affective computing and brain-computer interface applications. However, existing methods often face the challenge of achieving high classification accuracy while maintaining physiological interpretability.

Methods

In this study, we propose a convolutional neural network (CNN) model with a simple architecture for EEG-based emotion classification. The model achieves classification accuracies of 95.21% for low/high arousal, 94.59% for low/high valence, and 93.01% for quaternary classification tasks on the DEAP dataset. To further improve model interpretability and support practical applications, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to identify the EEG electrode regions that contribute most to the classification results.

Results

The visualization reveals that electrodes located in the right prefrontal cortex and left parietal lobe are the most influential, which is consistent with findings from emotional lateralization theory.

Conclusions

This provides a physiological basis for optimizing electrode placement in wearable EEG-based emotion recognition systems. The proposed method combines high classification performance with interpretability and provides guidance for the design of efficient and portable affective computing systems.