Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

Swati Prasad; Zheng-Hua Tan; Ramjee Prasad; Alvaro Rodrigo Cabrera; Ying Gu; Kim Dremstrup

Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

Swati Prasad, Zheng-Hua Tan, Ramjee Prasad, Alvaro Rodrigo Cabrera, Ying Gu, Kim Dremstrup

Research output: Contribution to journal › Conference article in Journal › Research › peer-review

Abstract

Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we present a feature selection strategy consisting of channel selection by fisher ratio analysis in the frequency domain and time segment selection by visual inspection in time domain. The proposed strategy achieves an absolute improvement of 7.5% in the misclassification rate as compared with the baseline system that uses wavelet coefficients as features and support vector machine (SVM) as classiﬁer

Original language	English
Journal	Proceedings of the Wireless Personal Multimedia Communications Symposia
Pages (from-to)	1-4
Number of pages	4
ISSN	1347-6890
Publication status	Published - 2011
Event	The 14th International Symposium on Wireless Personal Multimedia Communications - Brest, France Duration: 3 Oct 2011 → 6 Oct 2011

Conference

Conference	The 14th International Symposium on Wireless Personal Multimedia Communications
Country/Territory	France
City	Brest
Period	03/10/2011 → 06/10/2011

Keywords

Brain-computer interface, feature selection, EEG classification, discrete wavelet transform, support vector machine.

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@inproceedings{a98eed8a3da14e67934aade21b3d8366,

title = "Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery",

abstract = "Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we present a feature selection strategy consisting of channel selection by fisher ratio analysis in the frequency domain and time segment selection by visual inspection in time domain. The proposed strategy achieves an absolute improvement of 7.5% in the misclassification rate as compared with the baseline system that uses wavelet coefficients as features and support vector machine (SVM) as classiﬁer",

keywords = "Brain-computer interface, feature selection, EEG classification, discrete wavelet transform, support vector machine. ",

author = "Swati Prasad and Zheng-Hua Tan and Ramjee Prasad and Cabrera, {Alvaro Rodrigo} and Ying Gu and Kim Dremstrup",

year = "2011",

language = "English",

pages = "1--4",

journal = "Proceedings of the Wireless Personal Multimedia Communications Symposia",

issn = "1347-6890",

publisher = "IEEE Computer Society Press",

note = "The 14th International Symposium on Wireless Personal Multimedia Communications ; Conference date: 03-10-2011 Through 06-10-2011",

}

TY - GEN

T1 - Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

AU - Prasad, Swati

AU - Tan, Zheng-Hua

AU - Prasad, Ramjee

AU - Cabrera, Alvaro Rodrigo

AU - Gu, Ying

AU - Dremstrup, Kim

PY - 2011

Y1 - 2011

N2 - Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we present a feature selection strategy consisting of channel selection by fisher ratio analysis in the frequency domain and time segment selection by visual inspection in time domain. The proposed strategy achieves an absolute improvement of 7.5% in the misclassification rate as compared with the baseline system that uses wavelet coefficients as features and support vector machine (SVM) as classiﬁer

AB - Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we present a feature selection strategy consisting of channel selection by fisher ratio analysis in the frequency domain and time segment selection by visual inspection in time domain. The proposed strategy achieves an absolute improvement of 7.5% in the misclassification rate as compared with the baseline system that uses wavelet coefficients as features and support vector machine (SVM) as classiﬁer

KW - Brain-computer interface, feature selection, EEG classification, discrete wavelet transform, support vector machine.

M3 - Conference article in Journal

SN - 1347-6890

SP - 1

EP - 4

JO - Proceedings of the Wireless Personal Multimedia Communications Symposia

JF - Proceedings of the Wireless Personal Multimedia Communications Symposia

T2 - The 14th International Symposium on Wireless Personal Multimedia Communications

Y2 - 3 October 2011 through 6 October 2011

ER -

Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

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