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Expression EEG Multimodal Emotion Recognition Method Based on the Bidirectional LSTM and Attention Mechanism.

Yifeng Zhao¹, Deyun Chen¹

¹School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China.

Computational and Mathematical Methods in Medicine

|May 31, 2021

Summary

This study introduces a new multimodal emotion recognition method using facial expressions and electroencephalogram (EEG) signals. The approach enhances accuracy by employing a bidirectional LSTM with an attention mechanism for effective feature extraction and fusion.

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Area of Science:

Neuroscience
Computer Science
Artificial Intelligence

Background:

Human emotions are complex, leading to overlapping features in recognition methods.
Existing emotion recognition techniques struggle with accurate feature extraction and low precision.

Purpose of the Study:

To propose an improved multimodal emotion recognition method using facial expressions and EEG signals.
To enhance accuracy and overcome limitations of current emotion recognition systems.

Main Methods:

Facial expression features were extracted using a bilinear convolution network (BCN).
EEG signals were converted into frequency band image sequences and fused with expression features using BCN.
A three-layer bidirectional LSTM with an attention mechanism was utilized for feature fusion and timing modeling.

Main Results:

The proposed method effectively extracts emotion features from both expressions and EEG signals.
The attention mechanism improved image feature representation.
Experiments on MAHNOB-HCI and DEAP datasets demonstrated higher emotion recognition accuracy compared to existing methods.

Conclusions:

The integration of facial expression and EEG data with a bidirectional LSTM and attention mechanism significantly improves emotion recognition accuracy.
This multimodal approach offers a more effective way to understand and recognize human emotions.