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A novel channel selection method for multiple motion classification using high-density electromyography.

Geng Y, Zhang X, Zhang YT, Li G - Biomed Eng Online (2014)

Bottom Line: Selecting an appropriate number of surface electromyography (EMG) channels with desired classification performance and determining the optimal placement of EMG electrodes would be necessary and important in practical myoelectric control.Moreover, a fixed combination of the selected EMG channels was obtained when using MCCSP.The proposed MCCSP method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectric prostheses for limb amputees.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Human-Machine-Intelligence Synergic System of Chinese Academy of Sciences (CAS), Shenzhen Institutes of Advanced Technology (SIAT), CAS, Shenzhen, China. gl.li@siat.ac.cn.

ABSTRACT

Background: Selecting an appropriate number of surface electromyography (EMG) channels with desired classification performance and determining the optimal placement of EMG electrodes would be necessary and important in practical myoelectric control. In previous studies, several methods such as sequential forward selection (SFS) and Fisher-Markov selector (FMS) have been used to select the appropriate number of EMG channels for a control system. These exiting methods are dependent on either EMG features and/or classification algorithms, which means that when using different channel features or classification algorithm, the selected channels would be changed. In this study, a new method named multi-class common spatial pattern (MCCSP) was proposed for EMG selection in EMG pattern-recognition-based movement classification. Since MCCSP is independent on specific EMG features and classification algorithms, it would be more convenient for channel selection in developing an EMG control system than the exiting methods.

Methods: The performance of the proposed MCCSP method in selecting some optimal EMG channels (designated as a subset) was assessed with high-density EMG recordings from twelve mildly-impaired traumatic brain injury (TBI) patients. With the MCCSP method, a subset of EMG channels was selected and then used for motion classification with pattern recognition technique. In order to justify the performance of the MCCSP method against different electrode configurations, features and classification algorithms, two electrode configurations (unipolar and bipolar) as well as two EMG feature sets and two types of pattern recognition classifiers were considered in the study, respectively. And the performance of the proposed MCCSP method was compared with that of two exiting channel selection methods (SFS and FMS) in EMG control system.

Results: The results showed that in comparison with the previously used SFS and FMS methods, the newly proposed MCCSP method had better motion classification performance. Moreover, a fixed combination of the selected EMG channels was obtained when using MCCSP.

Conclusions: The proposed MCCSP method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectric prostheses for limb amputees.

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Related in: MedlinePlus

21 classes of forearm and hand movements included in the study.
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Figure 1: 21 classes of forearm and hand movements included in the study.

Mentions: Twelve mildly-impaired male patients with TBI participated in this study. They were chosen based on the upper limb motion impairment level assessed by a physical therapist. According to the definition of the stages in the Brunnstrom Assessment Scale, they were in stage IV-V and got the scores of 49 to 61 with the Fugl-Meyer Assessment of Sensorimotor Recovery after stroke, in which a zero score denotes no any function and a score of 66 designates as normal function. All of the subjects did not have any experience of attending this kind of research study before. In the experiment, they were asked to use their unilateral arm with severer motor impairment to perform 21 forearm and hand movements (Figure 1) plus one “no movement”. Each movement was maintained for 6 s with a moderate force and repeated 6 times. A rest time of 8 s was set between two successive movements in each trial. All subjects could choose to finish all or part of the 22 movements based on their own motor ability. The Research Ethics Board of the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, approved the experimental protocol of this study, and each subject gave written informed consent and provided permission for publication of photographs for a scientific and educational purpose.


A novel channel selection method for multiple motion classification using high-density electromyography.

Geng Y, Zhang X, Zhang YT, Li G - Biomed Eng Online (2014)

21 classes of forearm and hand movements included in the study.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4125347&req=5

Figure 1: 21 classes of forearm and hand movements included in the study.
Mentions: Twelve mildly-impaired male patients with TBI participated in this study. They were chosen based on the upper limb motion impairment level assessed by a physical therapist. According to the definition of the stages in the Brunnstrom Assessment Scale, they were in stage IV-V and got the scores of 49 to 61 with the Fugl-Meyer Assessment of Sensorimotor Recovery after stroke, in which a zero score denotes no any function and a score of 66 designates as normal function. All of the subjects did not have any experience of attending this kind of research study before. In the experiment, they were asked to use their unilateral arm with severer motor impairment to perform 21 forearm and hand movements (Figure 1) plus one “no movement”. Each movement was maintained for 6 s with a moderate force and repeated 6 times. A rest time of 8 s was set between two successive movements in each trial. All subjects could choose to finish all or part of the 22 movements based on their own motor ability. The Research Ethics Board of the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, approved the experimental protocol of this study, and each subject gave written informed consent and provided permission for publication of photographs for a scientific and educational purpose.

Bottom Line: Selecting an appropriate number of surface electromyography (EMG) channels with desired classification performance and determining the optimal placement of EMG electrodes would be necessary and important in practical myoelectric control.Moreover, a fixed combination of the selected EMG channels was obtained when using MCCSP.The proposed MCCSP method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectric prostheses for limb amputees.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Human-Machine-Intelligence Synergic System of Chinese Academy of Sciences (CAS), Shenzhen Institutes of Advanced Technology (SIAT), CAS, Shenzhen, China. gl.li@siat.ac.cn.

ABSTRACT

Background: Selecting an appropriate number of surface electromyography (EMG) channels with desired classification performance and determining the optimal placement of EMG electrodes would be necessary and important in practical myoelectric control. In previous studies, several methods such as sequential forward selection (SFS) and Fisher-Markov selector (FMS) have been used to select the appropriate number of EMG channels for a control system. These exiting methods are dependent on either EMG features and/or classification algorithms, which means that when using different channel features or classification algorithm, the selected channels would be changed. In this study, a new method named multi-class common spatial pattern (MCCSP) was proposed for EMG selection in EMG pattern-recognition-based movement classification. Since MCCSP is independent on specific EMG features and classification algorithms, it would be more convenient for channel selection in developing an EMG control system than the exiting methods.

Methods: The performance of the proposed MCCSP method in selecting some optimal EMG channels (designated as a subset) was assessed with high-density EMG recordings from twelve mildly-impaired traumatic brain injury (TBI) patients. With the MCCSP method, a subset of EMG channels was selected and then used for motion classification with pattern recognition technique. In order to justify the performance of the MCCSP method against different electrode configurations, features and classification algorithms, two electrode configurations (unipolar and bipolar) as well as two EMG feature sets and two types of pattern recognition classifiers were considered in the study, respectively. And the performance of the proposed MCCSP method was compared with that of two exiting channel selection methods (SFS and FMS) in EMG control system.

Results: The results showed that in comparison with the previously used SFS and FMS methods, the newly proposed MCCSP method had better motion classification performance. Moreover, a fixed combination of the selected EMG channels was obtained when using MCCSP.

Conclusions: The proposed MCCSP method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectric prostheses for limb amputees.

Show MeSH
Related in: MedlinePlus