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Wan Z, Yang R, Huang M, Alsaadi FE, Sheikh MM, Wang Z. Segment alignment based cross-subject motor imagery classification under fading data. Comput Biol Med 2022; 151:106267. [PMID: 36356391 DOI: 10.1016/j.compbiomed.2022.106267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/06/2022] [Accepted: 10/30/2022] [Indexed: 11/06/2022]
Abstract
Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.
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Affiliation(s)
- Zitong Wan
- Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China; Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Rui Yang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
| | - Mengjie Huang
- Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
| | - Fuad E Alsaadi
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Muntasir M Sheikh
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, United Kingdom
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Xu Z, Zhou Y, Wen X, Niu Y, Li Z, Xu X, Zhang D, Wu X. [ Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2022; 39:257-266. [PMID: 35523546 DOI: 10.7507/1001-5515.202105033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The research shows that personality assessment can be achieved by regression model based on electroencephalogram (EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures (positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What's more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.
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Affiliation(s)
- Ziming Xu
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
| | - Yueying Zhou
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
| | - Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
| | - Yifan Niu
- College of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China
- MOE Engineering Research Center for Intelligent Technology and Educational Application, Beijing Normal University, Beijing 100875, P. R. China
| | - Ziyu Li
- College of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China
- MOE Engineering Research Center for Intelligent Technology and Educational Application, Beijing Normal University, Beijing 100875, P. R. China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 211106, P. R. China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China
| | - Xia Wu
- College of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China
- MOE Engineering Research Center for Intelligent Technology and Educational Application, Beijing Normal University, Beijing 100875, P. R. China
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