Effects of Digital Filtering on the Classification Performance of Steady-State Visual Evoked Potential Based Brain-Computer Interfaces
Künye
Çetin, V., ÖZEKES, S., & VAROL, H. S. (2020). Effects of Digital Filtering on the Classification Performance of Steady-State Visual Evoked Potential Based Brain-Computer Interfaces. Balkan Journal of Electrical and Computer Engineering, 8(1), 108-113.Özet
The electrical activity that occurs during the
communication of neurons is recorded by a method called
electroencephalography. Brain computer interfaces utilize
various electrophysiological sources obtained from different
regions of the brain. The electrophysiological source used in this
study is the electrical activity seen in the occipital lobes as a result
of visual stimuli that flicker at certain frequencies, and is called
steady-state visual evoked potential. The main goal in this work is
not to try to improve the classification performance but to
investigate the effects of different digital filtering algorithms on
classification performance. The effects of the high pass and low
pass filtering on the classification performance in steady-state
visual evoked potential based brain computer interfaces are
investigated. As a result of this study, no significant change in the
classification performances of designs with only high pass
filtering, and high and low pass filtering, has been observed. In
addition, it has been observed that only the designs include a
high-pass filter implementation give better classification
performance in many cases. Consequently, it is concluded that
low-pass filtering in steady-state visual evoked potential based
brain-computer interfaces does not provide the desired
contribution to classification performance.