System based on subject-specific bands to recognize pedaling motor imagery: towards a BCI for lower-limb rehabilitation
Objective. The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. Approach. After applying a spectrogram based on s...
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| Published in | Journal of neural engineering Vol. 16; no. 5; pp. 56005 - 56020 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
IOP Publishing
01.10.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1741-2560 1741-2552 1741-2552 |
| DOI | 10.1088/1741-2552/ab08c8 |
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| Abstract | Objective. The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. Approach. After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability. Main results. For ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of and mean Kappa of . Significance. Our approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors. |
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| AbstractList | Objective. The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. Approach. After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability. Main results. For ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of and mean Kappa of . Significance. Our approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors. The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns.OBJECTIVEThe aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns.After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability.APPROACHAfter applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability.For ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of [Formula: see text] and mean Kappa of [Formula: see text].MAIN RESULTSFor ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of [Formula: see text] and mean Kappa of [Formula: see text].Our approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors.SIGNIFICANCEOur approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors. Objective . The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. Approach . After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability. Main results . For ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of and mean Kappa of . Significance . Our approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors. The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability. For ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of [Formula: see text] and mean Kappa of [Formula: see text]. Our approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors. |
| Author | Cardoso, Vivianne Delisle-Rodriguez, Denis Alejandra Romero-Laiseca, Maria Bastos-Filho, Teodiano Gurve, Dharmendra Krishnan, Sridhar Loterio, Flavia |
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| Snippet | Objective. The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to... Objective . The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods... The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve... |
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| SubjectTerms | brain-computer interface motor imagery neighborhood component analysis pedaling motor imagery Riemannian geometry sparseness constraints time-frequency analysis |
| Title | System based on subject-specific bands to recognize pedaling motor imagery: towards a BCI for lower-limb rehabilitation |
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