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Updated: Feb 26, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

MMSTG-Net: Multi-scale Spatiotemporal Graph Network with Cross-modal Contrastive Alignment for Multimodal Emotion

Jiayu Bi, Linjun Lu, Poly Z H Sun

IEEE Journal of Biomedical and Health Informatics

|February 24, 2026

Summary

This study introduces MMSTG-Net, a novel multimodal network for emotion recognition using EEG and eye-tracking. It shows promise as a research tool for clinical emotion assessment, pending further validation.

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Area of Science:

Neuroscience
Computer Science
Psychiatry

Background:

Current emotion recognition methods in psychiatry rely on subjective and time-limited self-reports.
There is a need for objective, fine-grained emotion assessment tools.

Purpose of the Study:

To develop a multimodal spatiotemporal graph network (MMSTG-Net) for precise emotion classification.
To jointly model temporal dynamics, brain connectivity, and cross-modal data from EEG and eye-tracking.

Main Methods:

Developed MMSTG-Net, a multimodal spatiotemporal graph network.
Utilized electroencephalography (EEG) and eye-tracking data.
Evaluated on SEED-VII and SEED-IV public datasets for seven-class emotion classification.

Main Results:

Achieved state-of-the-art performance on the SEED-VII dataset (82.74% subject-dependent, 63.06% cross-subject).
Obtained competitive results on the SEED-IV dataset (87.50% subject-dependent, 84.85% cross-subject).
Ablation studies confirmed the effectiveness of individual network modules and fusion design.

Conclusions:

MMSTG-Net demonstrates strong performance for emotion recognition using EEG and eye-tracking.
The model shows potential as a research tool to aid future clinical emotion assessment.
Further multi-center, prospective validation is required for clinical application.