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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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Number of selected EMG channels when using MCCSP. The red dots, blue diamonds, and yellow squares denote the number of selected monopolar EMG channels, the number of bipolar EMG channels, and the number of movement classes (nClass) completed by a subject, respectively.
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Figure 4: Number of selected EMG channels when using MCCSP. The red dots, blue diamonds, and yellow squares denote the number of selected monopolar EMG channels, the number of bipolar EMG channels, and the number of movement classes (nClass) completed by a subject, respectively.

Mentions: Figure 4 illustrates the numbers of selected EMG channels by MCCSP for all the 12 subjects when using EMG signals from monopolar and bipolar channel configurations, respectively. It can clearly observe from Figure 4 that the number of selected EMG channels varied for different subjects, ranging from 18 to 30 for the two channel configurations. And we can see that the numbers of selected channels by using monololar and bipolar EMG signals, respectively, were similar (in 10 subjects) or same (in two subjects). In order to make the following results comparable among all the subjects, the maximum number of selected EMG channels was set as 18. Note that for a subject the number of selected channels were corresponding to the movement classes that could be completed by him/her. Since the motor impairment of all the subjects was different, the number of completed movement classes (nClass designated by the yellow squares in Figure 4) would vary with a range of nClass = 19-22.


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)

Number of selected EMG channels when using MCCSP. The red dots, blue diamonds, and yellow squares denote the number of selected monopolar EMG channels, the number of bipolar EMG channels, and the number of movement classes (nClass) completed by a subject, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Number of selected EMG channels when using MCCSP. The red dots, blue diamonds, and yellow squares denote the number of selected monopolar EMG channels, the number of bipolar EMG channels, and the number of movement classes (nClass) completed by a subject, respectively.
Mentions: Figure 4 illustrates the numbers of selected EMG channels by MCCSP for all the 12 subjects when using EMG signals from monopolar and bipolar channel configurations, respectively. It can clearly observe from Figure 4 that the number of selected EMG channels varied for different subjects, ranging from 18 to 30 for the two channel configurations. And we can see that the numbers of selected channels by using monololar and bipolar EMG signals, respectively, were similar (in 10 subjects) or same (in two subjects). In order to make the following results comparable among all the subjects, the maximum number of selected EMG channels was set as 18. Note that for a subject the number of selected channels were corresponding to the movement classes that could be completed by him/her. Since the motor impairment of all the subjects was different, the number of completed movement classes (nClass designated by the yellow squares in Figure 4) would vary with a range of nClass = 19-22.

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