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Updated: Jul 2, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure.

Ming Gong¹, Wei Zhong², Long Ye²

¹Key Laboratory of Media Audio and Video (Communication University of China), Ministry of Education, Beijing, China.

Frontiers in Neuroscience

|February 29, 2024

Summary

This summary is machine-generated.

This study introduces a novel Multi-Source Information-Shared Network (MISNet) to improve subject-independent electroencephalography (EEG) emotion recognition by addressing domain shift. MISNet effectively disentangles private and shared emotional features for robust performance and adaptability.

Keywords:

EEG signals domain adaptation emotion recognition multi-source domain transfer learning

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Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Subject-independent electroencephalography (EEG) emotion recognition faces challenges due to domain shift across individuals.
Existing domain adaptation methods often lead to negative transfer by oversimplifying source domain relationships.

Purpose of the Study:

To propose a Multi-Source Information-Shared Network (MISNet) for enhanced subject-independent EEG emotion recognition.
To address the domain shift problem by incorporating individual differences and group commonalities.

Main Methods:

Developed a two-stream training structure with loop iteration for network stability.
Designed auxiliary loss functions with gradient penalty for feature distribution alignment.
Implemented a pre-training strategy for the shared encoder to capture emotional information.

Main Results:

Evaluated MISNet on SEED and SEED-IV datasets.
Demonstrated robust subject-independent performance and strong domain adaptability.
Confirmed the effectiveness of individual loss functions through ablation experiments.

Conclusions:

MISNet successfully disentangles private and shared emotional characteristics from EEG differential entropy features.
The proposed method achieves robust subject-independent emotion recognition with enhanced domain adaptability.