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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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Two electrode configurations. (a) 56-channel monopolar electrode configuration (b) 45-channel bipolar electrode configuration.
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Figure 3: Two electrode configurations. (a) 56-channel monopolar electrode configuration (b) 45-channel bipolar electrode configuration.

Mentions: The EMG signals recorded with 56 electrodes were digitally filtered with a five-order Butterworth high-pass filter at 30 Hz, and then the 50 Hz power line interference was reduced with a notch filter from EMG recordings. Two electrode configurations, monopolar mode and bipolar mode, were considered in the study with an attempt to see if the two modes of EMG signals would provide different performance in classifying the movements. The raw recordings were 56 monopolar EMG signals (Figure 3(a)). The bipolar EMG recordings were formed with the differential values between two adjacent monopolar channels along the orientation of muscle fibers from the 56 monopolar EMG signals, resulting in 45-channel bipolar EMG signals, as shown in Figure 3(b).


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)

Two electrode configurations. (a) 56-channel monopolar electrode configuration (b) 45-channel bipolar electrode configuration.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Two electrode configurations. (a) 56-channel monopolar electrode configuration (b) 45-channel bipolar electrode configuration.
Mentions: The EMG signals recorded with 56 electrodes were digitally filtered with a five-order Butterworth high-pass filter at 30 Hz, and then the 50 Hz power line interference was reduced with a notch filter from EMG recordings. Two electrode configurations, monopolar mode and bipolar mode, were considered in the study with an attempt to see if the two modes of EMG signals would provide different performance in classifying the movements. The raw recordings were 56 monopolar EMG signals (Figure 3(a)). The bipolar EMG recordings were formed with the differential values between two adjacent monopolar channels along the orientation of muscle fibers from the 56 monopolar EMG signals, resulting in 45-channel bipolar EMG signals, as shown in Figure 3(b).

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