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Updated: May 13, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

CIT-EmotionNet: convolution interactive transformer network for EEG emotion recognition.

Wei Lu^1,2,3, Lingnan Xia¹, Tien Ping Tan²

¹Henan High-Speed Railway Operation and Maintenance Engineering Research Center, Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, China.

Peerj. Computer Science

|February 3, 2025

Summary

This summary is machine-generated.

This study introduces CIT-EmotionNet, a new model for recognizing emotions using electroencephalogram (EEG) signals. It effectively combines global and local EEG features, achieving high accuracy in emotion recognition.

Keywords:

Affective computing Convolutional neural network (CNN)Electroencephalogram (EEG)Emotion recognition Transformer

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Area of Science:

Affective computing
Neuroscience
Artificial Intelligence

Background:

Emotion recognition is crucial in affective computing with diverse applications.
Electroencephalogram (EEG) signals offer a pathway for identifying human emotions.
Integrating global and local EEG features remains a significant challenge for improving recognition accuracy.

Purpose of the Study:

To propose a novel Convolution Interactive Transformer Network (CIT-EmotionNet) for enhanced EEG-based emotion recognition.
To efficiently integrate and fuse global and local features from EEG signals.
To improve the performance of emotion recognition systems.

Main Methods:

EEG signals were converted into spatial-spectral representations for model input.
A Convolution Interactive Transformer module was developed, integrating Convolutional Neural Networks (CNN) and Transformer architectures.
The module facilitates parallel extraction and interaction of local (CNN) and global (Transformer) features.

Main Results:

The proposed CIT-EmotionNet achieved high average recognition accuracies.
Achieved 98.57% accuracy on the SEED dataset.
Achieved 92.09% accuracy on the SEED-IV dataset.

Conclusions:

CIT-EmotionNet effectively integrates global and local EEG features for superior emotion recognition.
The novel Convolution Interactive Transformer module enhances feature interaction and fusion.
The model demonstrates state-of-the-art performance on benchmark EEG emotion recognition datasets.