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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 channel selection methods in terms of classification accuracy. Both (a) monopolar electrode configuration and (b) bipolar electrode configuration were used, respectively.
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Figure 7: Comparison of channel selection methods in terms of classification accuracy. Both (a) monopolar electrode configuration and (b) bipolar electrode configuration were used, respectively.

Mentions: Figure 7 shows the comparison of average classification accuracy across all subjects when using the EMG subsets (1-18 channels) selected by MCCSP, SFS, and FMS and their corresponding optimal feature-classifier combinations. The results indicate that applying TD-KNN to the EMG subset determined by MCCSP brought the highest classification accuracy and the best convergence when 4 to 13 optimal monopolar electrodes (Figure 7(a)) and 2 to 18 optimal bipolar electrodes (Figure 7(b)) were utilized. But the difference was not significant (p-value > 0.05).Figure 8 compares the two electrode configurations in terms of motion classification accuracy, which was obtained by using the EMG subset selected via MCCSP and the TD-KNN feature-classifier combination. The results show that the bipolar configuration was consistently better than the monopolar configuration with the increase of the number of included EMG channels. In addition, 18 EMG channels might be sufficient to get a proper motion classification performance. Because the average classification accuracy was 93.03% when using 18 optimal monopolar EMG channels, only 1.47% lower than that when using all 56 monopolar EMG channels (94.50%), and 18 optimal bipolar EMG channels brought an average classification accuracy of 95.58%, only 2.59% lower than that when using all 45 bipolar EMG channels (98.17%).


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 channel selection methods in terms of classification accuracy. Both (a) monopolar electrode configuration and (b) bipolar electrode configuration were used, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Comparison of channel selection methods in terms of classification accuracy. Both (a) monopolar electrode configuration and (b) bipolar electrode configuration were used, respectively.
Mentions: Figure 7 shows the comparison of average classification accuracy across all subjects when using the EMG subsets (1-18 channels) selected by MCCSP, SFS, and FMS and their corresponding optimal feature-classifier combinations. The results indicate that applying TD-KNN to the EMG subset determined by MCCSP brought the highest classification accuracy and the best convergence when 4 to 13 optimal monopolar electrodes (Figure 7(a)) and 2 to 18 optimal bipolar electrodes (Figure 7(b)) were utilized. But the difference was not significant (p-value > 0.05).Figure 8 compares the two electrode configurations in terms of motion classification accuracy, which was obtained by using the EMG subset selected via MCCSP and the TD-KNN feature-classifier combination. The results show that the bipolar configuration was consistently better than the monopolar configuration with the increase of the number of included EMG channels. In addition, 18 EMG channels might be sufficient to get a proper motion classification performance. Because the average classification accuracy was 93.03% when using 18 optimal monopolar EMG channels, only 1.47% lower than that when using all 56 monopolar EMG channels (94.50%), and 18 optimal bipolar EMG channels brought an average classification accuracy of 95.58%, only 2.59% lower than that when using all 45 bipolar EMG channels (98.17%).

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