Publication: A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation
Issued Date
2017-06-01
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ISSN
17410444
01400118
01400118
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2-s2.0-84988625058
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Mahidol University
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SCOPUS
Bibliographic Citation
Medical and Biological Engineering and Computing. Vol.55, No.6 (2017), 965-977
Suggested Citation
Yunyong Punsawad, Yodchanan Wongsawat A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation. Medical and Biological Engineering and Computing. Vol.55, No.6 (2017), 965-977. doi:10.1007/s11517-016-1560-3 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/42343
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Title
A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation
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Abstract
© 2016, International Federation for Medical and Biological Engineering. Steady-state visual evoked potentials (SSVEPs) are widely employed in brain–computer interface (BCI) applications, especially to control machines. However, the use of SSVEPs leads to eye fatigue and causes lower accuracy over the long term, particularly when multi-commands are required. Therefore, this paper proposes a half-field steady-state visual stimulation pattern and paradigm to increase the limited number of commands that can be achieved with existing SSVEP-based BCI methods. Following the theory of vision perception and existing half-field SSVEP-based BCI systems, the new stimulation pattern generates four commands using only one frequency flickering stimulus and has an average classification accuracy of approximately 75 %. According to the proposed stimulus pattern, using only one frequency without requiring users to stare directly at the flickering stimulus allows users to easily focus on the system and experience less visual fatigue compared to existing systems. Furthermore, new half-field SSVEP-based BCI systems are proposed, incorporating our proposed feature extraction and decision-making algorithm. Extracting the signal from the occipital area and using a reference electrode position at the parietal area yielded better results compared to the central area. In addition, we recommend using an LED or LCD as the visual stimulus device (at the recommended size), which yielded comparable results to our proposed feature extraction and decision-making algorithm. Finally, an application of the proposed system is demonstrated for real-time television control.