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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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Comparison of feature-classifier combinations in terms of classification performance. The average classification accuracy across all subjects was calculated when using four different feature-classifier combinations and three different EMG subsets selected via MCCSP (a)(d), SFS (b)(e) and FMS (c)(f), respectively. Both monpolar electrode configuration (a)(b)(c) and bipolar electrode configuration (d)(e)(f) were considered.
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Figure 5: Comparison of feature-classifier combinations in terms of classification performance. The average classification accuracy across all subjects was calculated when using four different feature-classifier combinations and three different EMG subsets selected via MCCSP (a)(d), SFS (b)(e) and FMS (c)(f), respectively. Both monpolar electrode configuration (a)(b)(c) and bipolar electrode configuration (d)(e)(f) were considered.

Mentions: To find the optimal feature-classifier combination for each selected EMG subset, the average classification accuracy across all subjects were calculated and shown in Figure 5 when using TD-LDA, TDAR-LDA, TD-KNN, and TDAR-KNN, respectively. The x-axis denoted the number of involved EMG channels that were selected by using MCCSP (Figure 5(a) and (d)), SFS (Figure 5(b) and (e)), and FMS (Figure 5(c) and (f)), respectively. These results show that with monopolar configuration (Figure 5(a)-(c)), TD-KNN outperformed other three feature-classifier combinations when using the EMG subsets determined by MCCSP and FMS, while TDAR-LDA was the best feature-classifier combination when using the EMG subset selected by SFS. It was almost the same case for bipolar configuration (Figure 5(d)-(f)). The difference was that when using the EMG subset selected by MCCSP, TDAR-KNN and TD-KNN had almost the same classification performance. In addition, when using the EMG subset determined by SFS, the difference among all the four feature-classifier combinations became smaller.We further investigated whether the feature set, classifier, or the combination of feature and classifier had a significant impact on the classification performance when using the EMG subset determined by MCCSP (Figure 6(a) and (b)), SFS (Figure 6(c) and (d)), and FMS (Figure 6(e) and (f)), respectively. The results demonstrate that when using the monopolar EMG channels determined by MCCSP (Figure 6(a)), the monopolar EMG channels determined by SFS (Figure 6(c)), and the bipolar EMG channels determined by SFS (Figure 6(d)), the classification performance was relatively stable with respect to different feature sets and classifiers. However, when using the bipolar EMG channels selected by MCCSP (Figure 6(b)), the monopolar EMG channels selected by FMS (Figure 6(e)), and the bipolar EMG channels selected by FMS (Figure 6(f)), the classification performance became sensitive to the choice of feature set and/or classifier.


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)

Comparison of feature-classifier combinations in terms of classification performance. The average classification accuracy across all subjects was calculated when using four different feature-classifier combinations and three different EMG subsets selected via MCCSP (a)(d), SFS (b)(e) and FMS (c)(f), respectively. Both monpolar electrode configuration (a)(b)(c) and bipolar electrode configuration (d)(e)(f) were considered.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Comparison of feature-classifier combinations in terms of classification performance. The average classification accuracy across all subjects was calculated when using four different feature-classifier combinations and three different EMG subsets selected via MCCSP (a)(d), SFS (b)(e) and FMS (c)(f), respectively. Both monpolar electrode configuration (a)(b)(c) and bipolar electrode configuration (d)(e)(f) were considered.
Mentions: To find the optimal feature-classifier combination for each selected EMG subset, the average classification accuracy across all subjects were calculated and shown in Figure 5 when using TD-LDA, TDAR-LDA, TD-KNN, and TDAR-KNN, respectively. The x-axis denoted the number of involved EMG channels that were selected by using MCCSP (Figure 5(a) and (d)), SFS (Figure 5(b) and (e)), and FMS (Figure 5(c) and (f)), respectively. These results show that with monopolar configuration (Figure 5(a)-(c)), TD-KNN outperformed other three feature-classifier combinations when using the EMG subsets determined by MCCSP and FMS, while TDAR-LDA was the best feature-classifier combination when using the EMG subset selected by SFS. It was almost the same case for bipolar configuration (Figure 5(d)-(f)). The difference was that when using the EMG subset selected by MCCSP, TDAR-KNN and TD-KNN had almost the same classification performance. In addition, when using the EMG subset determined by SFS, the difference among all the four feature-classifier combinations became smaller.We further investigated whether the feature set, classifier, or the combination of feature and classifier had a significant impact on the classification performance when using the EMG subset determined by MCCSP (Figure 6(a) and (b)), SFS (Figure 6(c) and (d)), and FMS (Figure 6(e) and (f)), respectively. The results demonstrate that when using the monopolar EMG channels determined by MCCSP (Figure 6(a)), the monopolar EMG channels determined by SFS (Figure 6(c)), and the bipolar EMG channels determined by SFS (Figure 6(d)), the classification performance was relatively stable with respect to different feature sets and classifiers. However, when using the bipolar EMG channels selected by MCCSP (Figure 6(b)), the monopolar EMG channels selected by FMS (Figure 6(e)), and the bipolar EMG channels selected by FMS (Figure 6(f)), the classification performance became sensitive to the choice of feature set and/or classifier.

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