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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Madaveeranahally Boregowda Anandaraju
BGS Institute of Technology
Economics, Econometrics & Finance

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Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system Manjula Krishnappa; Madaveeranahally Boregowda Anandaraju
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp215-221

Abstract

Brain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods.
Co-Authors Manjula Krishnappa