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

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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