Subject sensitive EEG discrimination with fast reconstructable CNN driven by reinforcement learning: A case study of ASD evaluation
Recent Electroencephalogram (EEG) analysis in connection with brain disorders has been tremendously benefiting from the (Deep) Neural Network technology in neuroscience research and neuro-engineering practices. However, the performance of existing hand-crafted models, such as the stability, has larg...
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| Published in: | Neurocomputing (Amsterdam) Vol. 449; pp. 136 - 145 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
18.08.2021
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Recent Electroencephalogram (EEG) analysis in connection with brain disorders has been tremendously benefiting from the (Deep) Neural Network technology in neuroscience research and neuro-engineering practices. However, the performance of existing hand-crafted models, such as the stability, has largely been refrained. This is the case especially in the paradigms that sensitive to the individuality of subjects and the non-stationarity of cognitive dynamics, such as Autism Spectrum Disorder (ASD) evaluation.
Aiming at this problem, this study develops a Q-Learning method to enable fast reconstruction of Convolutional Neural Network (CNN) thus to support EEG discrimination adapting to the individuality of subjects under examination. The proposed method first generates a CNN model with the structure and hyper-parameters determined (i.e., Neural Architecture Search) by the customized Q-Learning algorithm, where the CNN model is treated as a discrete system to be optimized. With the sharp shift of subjects, the Q-Learning algorithm reconstructs the CNN model to reach optimization reusing the tacit knowledge learned from the previous trials.
A case study has been performed to check the proposed method versus state-of-the-art counterparts based on resting-state EEG collected from 175 ASD-suspicious children with a diverse geological distribution. The observations in the case study indicate that: 1) the method outperforms the counterparts with an individual/sample accuracy of 92.63%/83.23% achieved; 2) the method can quickly reconstruct the CNN model with the group of subjects shifting from one region to another to maintain an encouraging performance while the counterparts without reconstruction may drop by about 12%. |
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| AbstractList | Recent Electroencephalogram (EEG) analysis in connection with brain disorders has been tremendously benefiting from the (Deep) Neural Network technology in neuroscience research and neuro-engineering practices. However, the performance of existing hand-crafted models, such as the stability, has largely been refrained. This is the case especially in the paradigms that sensitive to the individuality of subjects and the non-stationarity of cognitive dynamics, such as Autism Spectrum Disorder (ASD) evaluation.
Aiming at this problem, this study develops a Q-Learning method to enable fast reconstruction of Convolutional Neural Network (CNN) thus to support EEG discrimination adapting to the individuality of subjects under examination. The proposed method first generates a CNN model with the structure and hyper-parameters determined (i.e., Neural Architecture Search) by the customized Q-Learning algorithm, where the CNN model is treated as a discrete system to be optimized. With the sharp shift of subjects, the Q-Learning algorithm reconstructs the CNN model to reach optimization reusing the tacit knowledge learned from the previous trials.
A case study has been performed to check the proposed method versus state-of-the-art counterparts based on resting-state EEG collected from 175 ASD-suspicious children with a diverse geological distribution. The observations in the case study indicate that: 1) the method outperforms the counterparts with an individual/sample accuracy of 92.63%/83.23% achieved; 2) the method can quickly reconstruct the CNN model with the group of subjects shifting from one region to another to maintain an encouraging performance while the counterparts without reconstruction may drop by about 12%. |
| Author | Ke, Hengjin Dong, Heyou Zhang, Lei Chen, Dan Li, Xiaoli |
| Author_xml | – sequence: 1 givenname: Heyou surname: Dong fullname: Dong, Heyou organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 2 givenname: Dan surname: Chen fullname: Chen, Dan email: dan.chen@whu.edu.cn organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 3 givenname: Lei surname: Zhang fullname: Zhang, Lei organization: School of Computer Science, Wuhan University, Wuhan 430072, China – sequence: 4 givenname: Hengjin surname: Ke fullname: Ke, Hengjin organization: School of Computer Science, Hubei Polytechnic University, Huangshi 435003, China – sequence: 5 givenname: Xiaoli surname: Li fullname: Li, Xiaoli email: xiaoli@bnu.edu.cn organization: National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China |
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