A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

Journal of NeuroEngineering and Rehabilitation, Jan 2017

Background Most of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses. Methods Four transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method. Results The classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel EEG) were obtained with classification accuracies of 84.2 and 87.0%, respectively, which were about 7.2 and 10% higher than the accuracy by using only 32-channel sEMG input. Conclusions This study demonstrated the feasibility of fusing sEMG and EEG signals towards improving motion classification accuracy for above-elbow amputees, which might enhance the control performances of multifunctional myoelectric prostheses in clinical application. Trial registration The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.

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A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

Li et al. Journal of NeuroEngineering and Rehabilitation A motion-classification strategy based on sEMG-EEG signal combination for upper- limb amputees Xiangxin Li 0 2 Oluwarotimi Williams Samuel 0 2 Xu Zhang 0 1 Hui Wang 0 2 Peng Fang 0 Guanglin Li 0 0 Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology , Shenzhen 518055 , China 1 Department of Biology, South University of Science and Technology of China , Shenzhen 518055 , China 2 Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences , Shenzhen 518055 , China Background: Most of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses. Methods: Four transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method. Results: The classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel EEG) were obtained with classification accuracies of 84.2 and 87.0%, respectively, which were about 7.2 and 10% higher than the accuracy by using only 32-channel sEMG input. Conclusions: This study demonstrated the feasibility of fusing sEMG and EEG signals towards improving motion classification accuracy for above-elbow amputees, which might enhance the control performances of multifunctional myoelectric prostheses in clinical application. Amputee; EEG; Hybrid interface; Motion classification; Multifunctional prosthesis; Pattern recognition; Rehabilitation; sEMG; Signal Fusion - Trial registration: The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077. Background Multifunctional prostheses are commonly used by upper-limb amputees to restore their lost motion functions. Surface electromyography (sEMG) is a kind of neural signal that contains motor commands and can be non-invasively extracted on the muscle surface of residual limbs. Due to its relative ease of acquisition and abundant content of neural information, sEMG plays an important role in the control of modern motorized prostheses [1–4] and rehabilitation robotics [4–6]. In actually applications, however, the residual muscles after amputations are usually limited, especially in the case of above-elbow amputations. Thus, there exists a dilemma that the less the residual muscles are available for prosthesis control, the more the joint movements would be expected. As a result, multifunctional myoelectric prostheses for above-elbow amputees are still seldom seen on the market [7, 8]. Electroencephalography (EEG) is another kind of neural signal that contains the information related to mental activities of brain but is independent of amputation conditions [9]. Several efforts have been exploited to apply EEG as a brain-computer interface (BCI) for possible applications: Hochberg et al. applied EEG to control robotic arms to perform hand movements for paralyzed subjects, by decoding EEG signals recorded with microelectrode array implanted in the motor cortex [10]; Gernot et al. developed a non-invasive BCI system based on the steady-state visual evoked potentials (SSVEPs) to control a prosthetic hand, where a varied classification (...truncated)


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Xiangxin Li, Oluwarotimi Samuel, Xu Zhang, Hui Wang, Peng Fang, Guanglin Li. A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees, Journal of NeuroEngineering and Rehabilitation, 2017, pp. 2, 14, DOI: 10.1186/s12984-016-0212-z