SSVEP 기반 뇌-컴퓨터 인터페이스의 성능 향상을 위한 적응형 필터링 기반 효율적 잡음 제거 기법
Efficient Artifact Removal Method to Enhance SSVEP-based BCI Performance Based on Adaptive Filtering
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초록
This study investigates efficient artifact removal methods based on adaptive filtering to improve the per- formance of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. We utilized a custom-designed helmet equipped with a head-mounted display (HMD) to provide visual stimuli and electrodes to measure electroencephalography (EEG) signals from the occipital region as well as bio-signals from facial regions. We compared various filtering methods including band-pass filtering (BPF), normalized least mean square (NLMS), and recursive least square (RLS) adaptive filtering, and evaluated their performance in terms of classification accu- racy using canonical correlation analysis (CCA) to identify the target frequency of the SSVEP. Results showed that the combination of BPF and NLMS adaptive filtering achieved the highest classification accuracy of 97.19%. More- over, the choice of reference signal channels was important to the performance, showing electrodes placed below the eyes being more effective than those above. In conclusion, these findings demonstrate that incorporating efficient adaptive filtering methods and selecting appropriate reference channels can improve the accuracy and reliability of SSVEP-BCI systems.
Abstract
This study investigates efficient artifact removal methods based on adaptive filtering to improve the per- formance of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. We utilized a custom-designed helmet equipped with a head-mounted display (HMD) to provide visual stimuli and electrodes to measure electroencephalography (EEG) signals from the occipital region as well as bio-signals from facial regions. We compared various filtering methods including band-pass filtering (BPF), normalized least mean square (NLMS), and recursive least square (RLS) adaptive filtering, and evaluated their performance in terms of classification accu- racy using canonical correlation analysis (CCA) to identify the target frequency of the SSVEP. Results showed that the combination of BPF and NLMS adaptive filtering achieved the highest classification accuracy of 97.19%. More- over, the choice of reference signal channels was important to the performance, showing electrodes placed below the eyes being more effective than those above. In conclusion, these findings demonstrate that incorporating efficient adaptive filtering methods and selecting appropriate reference channels can improve the accuracy and reliability of SSVEP-BCI systems.
참고문헌 (45)
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