Identifying relevant EEG channels for subject-independent emotion recognition using attention network layers.

ABSTRACT:

Background

Electrical activity recorded with electroencephalography (EEG) enables the development of predictive models for emotion recognition. These models can be built using two approaches: subject-dependent and subject-independent. Although subject-independent models offer greater practical utility compared to subject-dependent models, they face challenges due to the significant variability of EEG signals between individuals.

Objective

One potential solution to enhance subject-independent approaches is to identify EEG channels that are consistently relevant across different individuals for predicting emotion. With the growing use of deep learning in emotion recognition, incorporating attention mechanisms can help uncover these shared predictive patterns.

Methods

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SUBMITTER: Valderrama CE 

PROVIDER: S-EPMC11847823 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature