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Fan C, Yang B, Li X, Zan P. Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement. Front Neurosci 2023; 17:1250991. [PMID: 37700746 PMCID: PMC10493321 DOI: 10.3389/fnins.2023.1250991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/15/2023] [Indexed: 09/14/2023] Open
Abstract
Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.
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Affiliation(s)
- Chengcheng Fan
- School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
- School of Medical Instrument, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Banghua Yang
- School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Xiaoou Li
- School of Medical Instrument, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Peng Zan
- School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
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Lei Y, Wang D, Wang W, Qu H, Wang J, Shi B. Improving single-hand open/close motor imagery classification by error-related potentials correction. Heliyon 2023; 9:e18452. [PMID: 37520987 PMCID: PMC10382287 DOI: 10.1016/j.heliyon.2023.e18452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 07/15/2023] [Accepted: 07/18/2023] [Indexed: 08/01/2023] Open
Abstract
Objective The ability of a brain-computer interface (BCI) to classify brain activity in electroencephalograms (EEG) during motor imagery (MI) tasks is an important performance indicator. Because the cortical regions that drive the single-handed open and closed tasks overlap, it is difficult to classify the EEG signals during executing both tasks. Approach The addition of special EEG features can improve the accuracy of classifying single-hand open and closed tasks. In this work, we designed a hybrid BCI paradigm based on error-related potentials (ErrP) and motor imagery (MI) and proposed a strategy to correct the classification results of MI by using ErrP information. The ErrP and MI features of EEG data from 11 subjects were superimposed. Main results The corrected strategy improved the classification accuracy of single-hand open/close MI tasks from 52.3% to 73.7%, an increase of approximately 21%. Significance Our hybrid BCI paradigm improves the classification accuracy of single-hand MI by adding ErrP information, which provides a new approach for improving the classification performance of BCI.
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Affiliation(s)
- Yanghao Lei
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi' an,710049, China
- Research Institute of NRR-Neurorehabilitation Robot, Xi' an Jiaotong University, Xi' an,710049, China
| | - Dong Wang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi' an,710049, China
- Research Institute of NRR-Neurorehabilitation Robot, Xi' an Jiaotong University, Xi' an,710049, China
| | - Weizhen Wang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi' an,710049, China
- Research Institute of NRR-Neurorehabilitation Robot, Xi' an Jiaotong University, Xi' an,710049, China
| | - Hao Qu
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi' an,710049, China
- Research Institute of NRR-Neurorehabilitation Robot, Xi' an Jiaotong University, Xi' an,710049, China
| | - Jing Wang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi' an,710049, China
- Research Institute of NRR-Neurorehabilitation Robot, Xi' an Jiaotong University, Xi' an,710049, China
| | - Bin Shi
- PLA Rocket Force University of Engineering Xi'an, Xi' an, 710025, China
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Liu G, Wang J. Dendrite Net: A White-Box Module for Classification, Regression, and System Identification. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:13774-13787. [PMID: 34793313 DOI: 10.1109/tcyb.2021.3124328] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called Dendrite Net or DD, just like the support vector machine (SVM) or multilayer perceptron (MLP). DD's main concept is that the algorithm can recognize this class after learning, if the output's logical expression contains the corresponding class's logical relationship among inputs (and \ or \ not). Experiments and main results: DD, a white-box ML algorithm, showed excellent system identification performance for the black-box system. Second, it was verified by nine real-world applications that DD brought better generalization capability relative to the MLP architecture that imitated neurons' cell body (Cell body Net) for regression. Third, by MNIST and FASHION-MNIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than the cell body net for classification. The number of modules can effectively adjust DD's logical expression capacity, which avoids overfitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in MATLAB and PyTorch (Python) demonstrated that DD was faster than Cell body Net both in epoch and forwardpropagation. The main contribution of this article is the basic ML algorithm (DD) with a white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at https://github.com/liugang1234567/Gang-neuron.
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Zhang X, Zhang S, Lu B, Wang Y, Li N, Peng Y, Hou J, Qiu J, Li F, Yao D, Xu P. Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis. J Neural Eng 2022; 19. [PMID: 35523144 DOI: 10.1088/1741-2552/ac6d7c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 05/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE A body movement involves the complicated information exchange between the central and peripheral systems, which is characterized by the dynamical coupling patterns between the multiple brain areas and multiple muscle units. How the central and peripheral nerves coordinate multiple internal brain regions and muscle groups is very important when accomplishing the action. APPROACH In this study, we extend the adaptive directed transfer function to construct the time-varying networks between multiple corticomuscular regions and divide the movement duration into different stages by the time-varying corticomuscular network patterns. MAIN RESULTS The inter dynamical corticomuscular network demonstrated the different interaction patterns between the central and peripheral systems during the different hand movement stages. The muscles transmit bottom-up movement information in the preparation stage, but the brain issues top-down control commands and dominates in the execution stage, and finally, the brain's dominant advantage gradually weakens in the relaxation stage. When classifying the different movement stages based on time-varying corticomuscular network indicators, an average accuracy above 74% could be reliably achieved. SIGNIFICANCE The findings of this study help deepen our knowledge of central-peripheral nerve pathways and coordination mechanisms, and also provide opportunities for monitoring and regulating movement disorders.
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Affiliation(s)
- Xiabing Zhang
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Shu Zhang
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Bin Lu
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Yifeng Wang
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Ning Li
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Yueheng Peng
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Jingming Hou
- Third Military Medical University Southwest Hospital, No. 30, Gaotanyanzheng Street, Shapingba District, Chongqing, 400038, CHINA
| | - Jing Qiu
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Fali Li
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Dezhong Yao
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
| | - Peng Xu
- University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, Chengdu, 610054, CHINA
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