CFBC: A Network for EEG Emotion Recognition by Selecting the Information of Crucial Frequency Bands

In this work, inspired by the frequency band division theory of electroencephalogram (EEG) signals, we propose the crucial frequency band convolution (CFBC) network method to explore the crucial frequency range of frequency domain features. CFBC includes two parts: spatial feature extractor and cruc...

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Published inIEEE sensors journal Vol. 24; no. 19; pp. 30451 - 30461
Main Authors Zhu, Mu, Bai, Zhongli, Wu, Qingzhou, Wang, Junchi, Xu, Wenhui, Song, Yu, Gao, Qiang
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Band theory
Brain modeling
Convolution
Convolutional neural networks
Crucial frequency band selector
Electrodes
Electroencephalography
electroencephalography (EEG)
Emotion recognition
Emotions
Feature extraction
Feature maps
Feature recognition
Frequencies
Frequency conversion
Frequency domain analysis
Frequency ranges
Position (location)
Power spectral density
Spatial data
spatial feature extractor
spatial-frequency feature
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Abstract In this work, inspired by the frequency band division theory of electroencephalogram (EEG) signals, we propose the crucial frequency band convolution (CFBC) network method to explore the crucial frequency range of frequency domain features. CFBC includes two parts: spatial feature extractor and crucial frequency band selector. First, we extracted differential entropy (DE) and power spectral density (PSD) features from the frequency domain of each EEG channel by different frequency bands. To avoid the loss of effective spatial information in processing, we interpolate the EEG feature maps by the relative location of electrodes. The spatial feature extractor captures spatial information using channel-by-channel convolution with the 2-D EEG feature maps containing electrode position information. The crucial frequency band selector performs causal dilated convolution for the selected frequency band sequence so that the features contained in different frequency bands are stacked into a feature vector. Finally, CFBC realizes the purpose of combining multiple selected frequency bands to extract the cross-frequency band feature vector. To evaluate the proposed method, we conducted a subject-dependent EEG emotion recognition experiment in the SEED dataset. The experimental result shows that the selection of a frequency band has an impact on the emotion classification effect of frequency domain features.
AbstractList In this work, inspired by the frequency band division theory of electroencephalogram (EEG) signals, we propose the crucial frequency band convolution (CFBC) network method to explore the crucial frequency range of frequency domain features. CFBC includes two parts: spatial feature extractor and crucial frequency band selector. First, we extracted differential entropy (DE) and power spectral density (PSD) features from the frequency domain of each EEG channel by different frequency bands. To avoid the loss of effective spatial information in processing, we interpolate the EEG feature maps by the relative location of electrodes. The spatial feature extractor captures spatial information using channel-by-channel convolution with the 2-D EEG feature maps containing electrode position information. The crucial frequency band selector performs causal dilated convolution for the selected frequency band sequence so that the features contained in different frequency bands are stacked into a feature vector. Finally, CFBC realizes the purpose of combining multiple selected frequency bands to extract the cross-frequency band feature vector. To evaluate the proposed method, we conducted a subject-dependent EEG emotion recognition experiment in the SEED dataset. The experimental result shows that the selection of a frequency band has an impact on the emotion classification effect of frequency domain features.
Author Xu, Wenhui
Gao, Qiang
Bai, Zhongli
Wang, Junchi
Zhu, Mu
Wu, Qingzhou
Song, Yu
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Snippet In this work, inspired by the frequency band division theory of electroencephalogram (EEG) signals, we propose the crucial frequency band convolution (CFBC)...
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SubjectTerms Band theory
Brain modeling
Convolution
Convolutional neural networks
Crucial frequency band selector
Electrodes
Electroencephalography
electroencephalography (EEG)
Emotion recognition
Emotions
Feature extraction
Feature maps
Feature recognition
Frequencies
Frequency conversion
Frequency domain analysis
Frequency ranges
Position (location)
Power spectral density
Spatial data
spatial feature extractor
spatial-frequency feature
Title CFBC: A Network for EEG Emotion Recognition by Selecting the Information of Crucial Frequency Bands
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Volume 24
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