ERTNet: an interpretable transformer-based framework for EEG emotion recognition.

ABSTRACT:

Background

Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.

Methods

We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between c

SUBMITTER: Liu R 

PROVIDER: S-EPMC10827927 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature