EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects

Jie Zhou; Jun Yao; Jie Deng; Julius P.A. Dewald

doi:10.1016/j.compbiomed.2009.02.004

EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects

Jie Zhou^*, Jun Yao, Jie Deng, Julius P.A. Dewald

^*Corresponding author for this work

Physical Therapy and Human Movement Sciences

Research output: Contribution to journal › Article › peer-review

45 Scopus citations

Abstract

The ultimate aim for classifying elbow versus shoulder torque intentions is to develop robust brain-computer interface (BCI) devices for patients who suffer from movement disorders following brain injury such as stroke. In this paper, we investigate the advanced classification approach classifier-enhanced time-frequency synthesized spatial pattern algorithm (classifier-enhanced TFSP) in classifying a subject's intent of generating an isometric shoulder abduction (SABD) or elbow flexion (EF) torque using signals obtained from 163 scalp electroencephalographic (EEG) electrodes. Two classifiers, the support vector classifier (SVC) and the classification and regression tree (CART), are integrated in the TFSP algorithm that decomposes the signal into a weighted time, frequency and spatial feature space. The resulting high-performing methods (SVC-TFSP and CART-TFSP) are then applied to experimental data collected in four healthy subjects and two stroke subjects. Results are compared with the original TFSP, and significantly higher reliability in both healthy subjects (92% averaged over four healthy subjects) and stroke subjects (75% averaged over two subjects) are achieved. The accuracies of classifier-enhanced TFSP methods are further improved after a rejection scheme is applied (∼100% in healthy subjects and >80% in stroke subjects). The results are among the highest reliability reported in literature for tasks with spatial representations on the motor cortex as close as shoulder and elbow. The paper also discusses the impact of applying rejection strategy in detail and reports the existence of an optimal rejection rate on a stroke subject. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients.

Original language	English (US)
Pages (from-to)	443-452
Number of pages	10
Journal	Computers in Biology and Medicine
Volume	39
Issue number	5
DOIs	https://doi.org/10.1016/j.compbiomed.2009.02.004
State	Published - May 2009

Funding

This research is supported by SDG (0435348Z) from American Heart Association, R03 (HD39804-01A1), R01 (5R01HD 39343-02) and R01 (5R01HD 047569-04) from NIH. The authors thank Mr. Albert Chen for the figure of EEG electrode montage presented in this paper. Jie Zhou received her BS and MS degrees in Biomedical Engineering from Southeast University, Nanjing, China, in 1993 and 1996, respectively, and her PhD degree in Computer Science from Concordia University, Montreal, Canada, in 2000. She has been an Assistant Professor since 2002 and an Associate Professor since 2008 in the Department of Computer Science at Northern Illinois University, USA. Her research interests include pattern recognition, machine intelligence and applications of computational methods in medicine and biology. Prof. Zhou was a recipient of FONDS F.C.A.R. (Fonds pour la Formation de Chercheurs et l’Aide a la Recherche) of Quebec, Canada. She has also been a recipient of Northern Illinois University Graduate School Research and Artistry Grants. Prof. Zhou is an Associate Editor of Pattern Recognition Journal.

Keywords

Brain-computer interface
Classification
Classification and regression tree
Elbow flexion
Electroencephalograph (EEG)
Shoulder abduction
Stroke
Support-vector machine
Time-frequency synthesized spatial pattern algorithm

ASJC Scopus subject areas

Health Informatics
Computer Science Applications

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10.1016/j.compbiomed.2009.02.004

Cite this

@article{d9ad187465b64baea94818a8e0c722d7,

title = "EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects",

abstract = "The ultimate aim for classifying elbow versus shoulder torque intentions is to develop robust brain-computer interface (BCI) devices for patients who suffer from movement disorders following brain injury such as stroke. In this paper, we investigate the advanced classification approach classifier-enhanced time-frequency synthesized spatial pattern algorithm (classifier-enhanced TFSP) in classifying a subject's intent of generating an isometric shoulder abduction (SABD) or elbow flexion (EF) torque using signals obtained from 163 scalp electroencephalographic (EEG) electrodes. Two classifiers, the support vector classifier (SVC) and the classification and regression tree (CART), are integrated in the TFSP algorithm that decomposes the signal into a weighted time, frequency and spatial feature space. The resulting high-performing methods (SVC-TFSP and CART-TFSP) are then applied to experimental data collected in four healthy subjects and two stroke subjects. Results are compared with the original TFSP, and significantly higher reliability in both healthy subjects (92% averaged over four healthy subjects) and stroke subjects (75% averaged over two subjects) are achieved. The accuracies of classifier-enhanced TFSP methods are further improved after a rejection scheme is applied (∼100% in healthy subjects and >80% in stroke subjects). The results are among the highest reliability reported in literature for tasks with spatial representations on the motor cortex as close as shoulder and elbow. The paper also discusses the impact of applying rejection strategy in detail and reports the existence of an optimal rejection rate on a stroke subject. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients.",

keywords = "Brain-computer interface, Classification, Classification and regression tree, Elbow flexion, Electroencephalograph (EEG), Shoulder abduction, Stroke, Support-vector machine, Time-frequency synthesized spatial pattern algorithm",

author = "Jie Zhou and Jun Yao and Jie Deng and Dewald, {Julius P.A.}",

note = "Funding Information: This research is supported by SDG (0435348Z) from American Heart Association, R03 (HD39804-01A1), R01 (5R01HD 39343-02) and R01 (5R01HD 047569-04) from NIH. The authors thank Mr. Albert Chen for the figure of EEG electrode montage presented in this paper. Funding Information: Jie Zhou received her BS and MS degrees in Biomedical Engineering from Southeast University, Nanjing, China, in 1993 and 1996, respectively, and her PhD degree in Computer Science from Concordia University, Montreal, Canada, in 2000. She has been an Assistant Professor since 2002 and an Associate Professor since 2008 in the Department of Computer Science at Northern Illinois University, USA. Her research interests include pattern recognition, machine intelligence and applications of computational methods in medicine and biology. Prof. Zhou was a recipient of FONDS F.C.A.R. (Fonds pour la Formation de Chercheurs et l{\textquoteright}Aide a la Recherche) of Quebec, Canada. She has also been a recipient of Northern Illinois University Graduate School Research and Artistry Grants. Prof. Zhou is an Associate Editor of Pattern Recognition Journal. ",

year = "2009",

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doi = "10.1016/j.compbiomed.2009.02.004",

language = "English (US)",

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T1 - EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects

AU - Zhou, Jie

AU - Yao, Jun

AU - Deng, Jie

AU - Dewald, Julius P.A.

N1 - Funding Information: This research is supported by SDG (0435348Z) from American Heart Association, R03 (HD39804-01A1), R01 (5R01HD 39343-02) and R01 (5R01HD 047569-04) from NIH. The authors thank Mr. Albert Chen for the figure of EEG electrode montage presented in this paper. Funding Information: Jie Zhou received her BS and MS degrees in Biomedical Engineering from Southeast University, Nanjing, China, in 1993 and 1996, respectively, and her PhD degree in Computer Science from Concordia University, Montreal, Canada, in 2000. She has been an Assistant Professor since 2002 and an Associate Professor since 2008 in the Department of Computer Science at Northern Illinois University, USA. Her research interests include pattern recognition, machine intelligence and applications of computational methods in medicine and biology. Prof. Zhou was a recipient of FONDS F.C.A.R. (Fonds pour la Formation de Chercheurs et l’Aide a la Recherche) of Quebec, Canada. She has also been a recipient of Northern Illinois University Graduate School Research and Artistry Grants. Prof. Zhou is an Associate Editor of Pattern Recognition Journal.

PY - 2009/5

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N2 - The ultimate aim for classifying elbow versus shoulder torque intentions is to develop robust brain-computer interface (BCI) devices for patients who suffer from movement disorders following brain injury such as stroke. In this paper, we investigate the advanced classification approach classifier-enhanced time-frequency synthesized spatial pattern algorithm (classifier-enhanced TFSP) in classifying a subject's intent of generating an isometric shoulder abduction (SABD) or elbow flexion (EF) torque using signals obtained from 163 scalp electroencephalographic (EEG) electrodes. Two classifiers, the support vector classifier (SVC) and the classification and regression tree (CART), are integrated in the TFSP algorithm that decomposes the signal into a weighted time, frequency and spatial feature space. The resulting high-performing methods (SVC-TFSP and CART-TFSP) are then applied to experimental data collected in four healthy subjects and two stroke subjects. Results are compared with the original TFSP, and significantly higher reliability in both healthy subjects (92% averaged over four healthy subjects) and stroke subjects (75% averaged over two subjects) are achieved. The accuracies of classifier-enhanced TFSP methods are further improved after a rejection scheme is applied (∼100% in healthy subjects and >80% in stroke subjects). The results are among the highest reliability reported in literature for tasks with spatial representations on the motor cortex as close as shoulder and elbow. The paper also discusses the impact of applying rejection strategy in detail and reports the existence of an optimal rejection rate on a stroke subject. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients.

AB - The ultimate aim for classifying elbow versus shoulder torque intentions is to develop robust brain-computer interface (BCI) devices for patients who suffer from movement disorders following brain injury such as stroke. In this paper, we investigate the advanced classification approach classifier-enhanced time-frequency synthesized spatial pattern algorithm (classifier-enhanced TFSP) in classifying a subject's intent of generating an isometric shoulder abduction (SABD) or elbow flexion (EF) torque using signals obtained from 163 scalp electroencephalographic (EEG) electrodes. Two classifiers, the support vector classifier (SVC) and the classification and regression tree (CART), are integrated in the TFSP algorithm that decomposes the signal into a weighted time, frequency and spatial feature space. The resulting high-performing methods (SVC-TFSP and CART-TFSP) are then applied to experimental data collected in four healthy subjects and two stroke subjects. Results are compared with the original TFSP, and significantly higher reliability in both healthy subjects (92% averaged over four healthy subjects) and stroke subjects (75% averaged over two subjects) are achieved. The accuracies of classifier-enhanced TFSP methods are further improved after a rejection scheme is applied (∼100% in healthy subjects and >80% in stroke subjects). The results are among the highest reliability reported in literature for tasks with spatial representations on the motor cortex as close as shoulder and elbow. The paper also discusses the impact of applying rejection strategy in detail and reports the existence of an optimal rejection rate on a stroke subject. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients.

KW - Brain-computer interface

KW - Classification

KW - Classification and regression tree

KW - Elbow flexion

KW - Electroencephalograph (EEG)

KW - Shoulder abduction

KW - Stroke

KW - Support-vector machine

KW - Time-frequency synthesized spatial pattern algorithm

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ER -

EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects

Abstract

Funding

Keywords

ASJC Scopus subject areas

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