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- W2950469471 abstract "This paper presents a novel approach of spectral feature selection using spatial filters for the classification of four cognitive imagery tasks. The input dataset consists of electroencephalogram (EEG) signals acquired through a commercial wireless headset. The spectral features included mel frequency (MF) components extracted from the low frequency bands of EEG signal. A spatial projection filter was used for the selection of the most relevant features before classification. The popular method of multiclass common spatial pattern (CSP) and regularized CSP (RCSP) are investigated for a subject dependent (intra) and subject independent (inter) generation of spatial projection filter, respectively. Based upon this, present study used two different algorithmic approaches namely MF-CSP and MF-RCSP. The developed algorithm successfully classified four imagery actions with the reported prediction accuracy of 46.23% and 64.01% and standard deviation of 11.60% and 8.67% for MF-CSP and MF-RCSP, respectively." @default.
- W2950469471 created "2019-06-27" @default.
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- W2950469471 date "2019-06-05" @default.
- W2950469471 modified "2023-09-29" @default.
- W2950469471 title "Cognitive Imagery Classification of EEG Signals using CSP-based Feature Selection Method" @default.
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- W2950469471 doi "https://doi.org/10.1080/02564602.2019.1620138" @default.
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