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Non-dyadic wavelet decomposition for sensory-motor imagery EEG classification

Non-dyadic wavelet decomposition for sensory-motor imagery EEG classification Identification of imagined motor movement during cerebral activity is a significant task for translating the activity into control signals for brain–computer interface-based applications. This paper proposes a model based on non-dyadic wavelet decomposition for specifying left-hand and right-hand movement detection using motor imagery EEG signals. Three key components define our model: (1) The preprocessing and non-dyadic wavelet decomposition of EEG signals, (2) feature extraction using the common spatial pattern (CSP) coefficients of wavelet decomposed signals, (3) classification of test signal using selected features. The wavelet decomposition is done on the basis of m-band filtering. Classification of extracted features is done using different classifiers and obtained results are compared in terms of sensitivity, specificity, accuracy, and the kappa value. The proposed model gives the highest classification accuracy of 85.6% using decision tree classifier for BCI Competition 4 dataset IIa and BCI Competition 3 dataset IVa. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Non-dyadic wavelet decomposition for sensory-motor imagery EEG classification

Brain-Computer Interfaces , Volume 7 (1-2): 11 – Apr 2, 2020

Non-dyadic wavelet decomposition for sensory-motor imagery EEG classification

Abstract

Identification of imagined motor movement during cerebral activity is a significant task for translating the activity into control signals for brain–computer interface-based applications. This paper proposes a model based on non-dyadic wavelet decomposition for specifying left-hand and right-hand movement detection using motor imagery EEG signals. Three key components define our model: (1) The preprocessing and non-dyadic wavelet decomposition of EEG signals, (2) feature extraction...
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Publisher
Taylor & Francis
Copyright
© 2020 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2020.1736453
Publisher site
See Article on Publisher Site

Abstract

Identification of imagined motor movement during cerebral activity is a significant task for translating the activity into control signals for brain–computer interface-based applications. This paper proposes a model based on non-dyadic wavelet decomposition for specifying left-hand and right-hand movement detection using motor imagery EEG signals. Three key components define our model: (1) The preprocessing and non-dyadic wavelet decomposition of EEG signals, (2) feature extraction using the common spatial pattern (CSP) coefficients of wavelet decomposed signals, (3) classification of test signal using selected features. The wavelet decomposition is done on the basis of m-band filtering. Classification of extracted features is done using different classifiers and obtained results are compared in terms of sensitivity, specificity, accuracy, and the kappa value. The proposed model gives the highest classification accuracy of 85.6% using decision tree classifier for BCI Competition 4 dataset IIa and BCI Competition 3 dataset IVa.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Apr 2, 2020

Keywords: Motor imagery classification; non-dyadic wavelet decomposition; common spatial pattern; classifiers

References