Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix

Ganesh R. Naik; Amit Acharyya; Hung T. Nguyen

doi:10.1109/EMBC.2014.6944458

Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix

Ganesh R. Naik, Amit Acharyya, Hung T. Nguyen

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

25 Citations (Scopus)

Abstract

This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.

Original language	English
Title of host publication	Proceedings of 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, Illinois, USA, 26-30 August 2014
Publisher	IEEE
Pages	3829-3832
Number of pages	4
ISBN (Print)	9781424479290
DOIs	https://doi.org/10.1109/EMBC.2014.6944458
Publication status	Published - 2014
Event	IEEE Engineering in Medicine and Biology Society. Annual Conference - Duration: 30 Apr 2015 → …

Conference

Conference	IEEE Engineering in Medicine and Biology Society. Annual Conference
Period	30/04/15 → …

Keywords

discriminant analysis
electromyography
fingers
independent component analysis
user-computer interface

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10.1109/EMBC.2014.6944458

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Naik, G. R., Acharyya, A., & Nguyen, H. T. (2014). Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix. In Proceedings of 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, Illinois, USA, 26-30 August 2014 (pp. 3829-3832). IEEE. https://doi.org/10.1109/EMBC.2014.6944458

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title = "Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix",

abstract = "This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.",

keywords = "discriminant analysis, electromyography, fingers, independent component analysis, user-computer interface",

author = "Naik, {Ganesh R.} and Amit Acharyya and Nguyen, {Hung T.}",

year = "2014",

doi = "10.1109/EMBC.2014.6944458",

language = "English",

isbn = "9781424479290",

pages = "3829--3832",

booktitle = "Proceedings of 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, Illinois, USA, 26-30 August 2014",

publisher = "IEEE",

note = "IEEE Engineering in Medicine and Biology Society. Annual Conference ; Conference date: 30-04-2015",

}

Naik, GR, Acharyya, A & Nguyen, HT 2014, Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix. in Proceedings of 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, Illinois, USA, 26-30 August 2014. IEEE, pp. 3829-3832, IEEE Engineering in Medicine and Biology Society. Annual Conference, 30/04/15. https://doi.org/10.1109/EMBC.2014.6944458

Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix. / Naik, Ganesh R.; Acharyya, Amit; Nguyen, Hung T.
Proceedings of 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, Illinois, USA, 26-30 August 2014. IEEE, 2014. p. 3829-3832.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix

AU - Naik, Ganesh R.

AU - Acharyya, Amit

AU - Nguyen, Hung T.

PY - 2014

Y1 - 2014

N2 - This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.

AB - This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.

KW - discriminant analysis

KW - electromyography

KW - fingers

KW - independent component analysis

KW - user-computer interface

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:42725

U2 - 10.1109/EMBC.2014.6944458

DO - 10.1109/EMBC.2014.6944458

M3 - Conference Paper

SN - 9781424479290

SP - 3829

EP - 3832

BT - Proceedings of 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), Chicago, Illinois, USA, 26-30 August 2014

PB - IEEE

T2 - IEEE Engineering in Medicine and Biology Society. Annual Conference

Y2 - 30 April 2015

ER -

Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix

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