Cognitive Imagery Classification of EEG Signals using CSP-based Feature Selection Method. Issue 3 (3rd May 2020)

• Record Type: Journal Article
• Authors: Hooda, Neha; Kumar, Neelesh
• Is Part Of: IETE technical review. Volume 37:Issue 3(2020)
• Page Start: 315
• Page End: 326
• Publication Date: 2020-05-03
• Subjects: Brain computer interface -- Cognitive imagery (CI) -- CSP (Common spatial pattern) -- EEG (Electroencephalography) -- Emotiv EPOC+ -- MFCC (Mel frequency cepstral coefficients); Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; 621.38
• DOI: 10.1080/02564602.2019.1620138
• Languages: English
• ISSNs: 0256-4602

Abstract

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.