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Emotion recognition with convolutional neural network and EEG-based EFDMs.

Fei Wang¹, Shichao Wu¹, Weiwei Zhang¹

¹Faculty of Robot Science and Engineering, Northeastern University, Shenyang, 110169, China.

Neuropsychologia

|June 5, 2020

Summary

This summary is machine-generated.

This study introduces electrode-frequency distribution maps (EFDMs) and deep learning for improved electroencephalogram (EEG) emotion recognition. The novel approach enhances accuracy and enables effective cross-dataset emotion detection.

Keywords:

Convolutional neural network Electrode-frequency distribution maps Electroencephalogram Emotion recognition Gradient-weighted class activation mapping

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Area of Science:

Neuroscience
Computer Science
Signal Processing

Background:

Electroencephalogram (EEG) signals reflect brain activity, useful for detecting mental states and conditions.
Traditional EEG emotion recognition faces challenges due to signal complexity, individual differences, and low accuracy.
Existing methods often require complex feature extraction, limiting their effectiveness.

Purpose of the Study:

To develop a novel method for accurate EEG-based emotion recognition.
To address limitations of traditional methods, including feature extraction complexity and low recognition rates.
To enable effective emotion recognition across different datasets using transfer learning.

Main Methods:

Proposed electrode-frequency distribution maps (EFDMs) using short-time Fourier transform (STFT).
Utilized a residual block-based deep convolutional neural network (CNN) for automatic feature extraction and classification.
Implemented deep model transfer learning for cross-dataset emotion recognition, addressing small sample sizes and individual variations.
Employed Gradient weighted Class Activation Mapping (Grad-CAM) for visualizing learned features.

Main Results:

Achieved 90.59% average classification score on the SEED dataset, outperforming the baseline by 4.51%.
Demonstrated an 82.84% average accuracy on the DEAP dataset using transfer learning with minimal samples.
Grad-CAM analysis indicated that high-frequency bands are crucial for emotion recognition from EFDMs.

Conclusions:

The proposed EFDM and CNN approach significantly improves EEG-based emotion recognition accuracy.
Deep model transfer learning offers a viable solution for cross-dataset emotion recognition with limited data.
High-frequency EEG components are key discriminative features for emotion classification.