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International Conference on Innovative Computing and Communications pp 611–622Cite as

Orthonormal Wavelet Transform for Efficient Feature Extraction for Sensory-Motor Imagery Electroencephalogram Brain–Computer Interface

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1166))

Abstract

Wavelet Transform (WT) is a well-known method for localizing frequency in time domain in transient and non-stationary signals like electroencephalogram (EEG) signals. These EEG signals are used for non-invasive Brain–Computer Interface (BCI) system design. Generally, the signals are decomposed in dyadic (two-band) frequency bands for frequency localization in time domain. The triadic approach involves the filtering of EEG signals into three frequency filter bands: low-pass filter, high-pass filter, and band-pass filter. The sensory-motor imagery (SMI) frequencies (α, β, and high γ) can be localized from non-stationary EEG signals in using this triadic wavelet filter efficiently. Further features can be extracted using common spatial pattern (CSP) algorithms and these features can be classified by machine learning algorithms. This paper discusses dyadic and non-dyadic filtering in detail and also proposes an approach for frequency localization using three-band orthogonal wavelet transformation for classification of sensory-motor imagery electroencephalogram (EEG) signals.

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Authors and Affiliations

  1. Manav Rachna International Institute of Research and Studies, Faridabad, India

    Poonam Chaudhary & Rashmi Agrawal

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  1. Poonam Chaudhary
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  2. Rashmi Agrawal
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Correspondence to Poonam Chaudhary .

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Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  2. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  3. CHRIST (Deemed to be University), Bengaluru, Karnataka, India

    Siddhartha Bhattacharyya

  4. Department of Information Technology, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  5. Department of Computer Science, Shaheed Sukhdev College of Business Studies, University of Delhi, Rohini, Delhi, India

    Sameer Anand

  6. Department of Computer Science, Shaheed Sukhdev College of Business Studies, University of Delhi, Rohini, Delhi, India

    Ajay Jaiswal

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Chaudhary, P., Agrawal, R. (2021). Orthonormal Wavelet Transform for Efficient Feature Extraction for Sensory-Motor Imagery Electroencephalogram Brain–Computer Interface. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_54

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