E2ENNet: An end-to-end neural network for emotional brain-computer interface
Objectve: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering c...
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| Published in | Frontiers in computational neuroscience Vol. 16; p. 942979 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Lausanne
Frontiers Research Foundation
12.08.2022
Frontiers Media S.A |
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| Abstract | Objectve: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet. Methods: Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions. Results: Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset. Conclusion: Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals. Significance: This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system. |
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| AbstractList | ObjectveEmotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet.MethodsBaseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions.ResultsExtensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset.ConclusionExperimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals.SignificanceThis study provides a methodology for implementing a plug-and-play emotional brain-computer interface system. Objectve: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet. Methods: Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions. Results: Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset. Conclusion: Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals. Significance: This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system. Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet.ObjectveEmotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet.Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions.MethodsBaseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions.Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset.ResultsExtensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset.Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals.ConclusionExperimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals.This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system.SignificanceThis study provides a methodology for implementing a plug-and-play emotional brain-computer interface system. |
| Author | Shao, Yongbin Han, Zhichao Chang, Hongli Wang, Lili Zhou, Xiaoyan Wang, Jihao |
| AuthorAffiliation | 2 The Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Southeast University , Nanjing , China 1 School of Electronic and Information Engineering, Nanjing University of Information Science and Technology , Nanjing , China |
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| ContentType | Journal Article |
| Copyright | 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2022 Han, Chang, Zhou, Wang, Wang and Shao. Copyright © 2022 Han, Chang, Zhou, Wang, Wang and Shao. 2022 Han, Chang, Zhou, Wang, Wang and Shao |
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| Snippet | Objectve: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness.... Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the... ObjectveEmotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However,... |
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| SubjectTerms | Accuracy Brain Classification Computer applications Datasets Deep learning depthwise separable convolution EEG Electrodes electroencephalogram (EEG) Electroencephalography emotional brain-computer interface Emotions Experiments Implants long short-term memory Mental disorders Neural networks neurocognitive Neuroscience |
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| Title | E2ENNet: An end-to-end neural network for emotional brain-computer interface |
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