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Title: A Single-Channel Consumer-Grade EEG Device for Brain-Computer Interface: Enhancing Detection of SSVEP and Its Amplitude Modulation
Authors: Phairot Autthasan
Xiangqian Du
Jetsada Arnin
Sirakorn Lamyai
Maneesha Perera
Sirawaj Itthipuripat
Tohru Yagi
Poramate Manoonpong
Theerawit Wilaiprasitporn
Vidyasirimedhi Institute of Science and Technology
Tokyo Institute of Technology
Kasetsart University
Mahidol University
Sirindhorn International Institute of Technology, Thammasat University
Vanderbilt University
Keywords: Engineering;Physics and Astronomy
Issue Date: 15-Mar-2020
Citation: IEEE Sensors Journal. Vol.20, No.6 (2020), 3366-3378
Abstract: © 2001-2012 IEEE. Brain-Computer interfaces (BCIs) play a significant role in easing neuromuscular patients on controlling computers and prosthetics. Due to their high signal-to-noise ratio, steady-state visually evoked potentials (SSVEPs) has been widely used to build BCIs. However, currently developed algorithms do not predict the modulation of SSVEP amplitude, which is known to change as a function of stimulus luminance contrast. In this study, we aim to develop an integrated approach to simultaneously estimate the frequency and contrast-related amplitude modulations of the SSVEP signal. To achieve that, we developed a behavioral task in which human participants focused on a visual flicking target which the luminance contrast can change through time in several ways. SSVEP signals from 16 subjects were then recorded from electrodes placed at the central occipital site using a low-cost, consumer-grade EEG. Our results demonstrate that the filter bank canonical correlation analysis (FBCCA) performed well in SSVEP frequency recognition, while the support vector regression (SVR) outperformed the other supervised machine learning algorithms in predicting the contrast-dependent amplitude modulations of the SSVEPs. These findings indicate the applicability and strong performance of our integrated method at simultaneously predicting both frequency and amplitude of visually evoked signals, and have proven to be useful for advancing SSVEP-based applications.
ISSN: 15581748
Appears in Collections:Scopus 2020

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