Abstract
The objective of this study is to evaluate a real-time Brain computer interface (BCI) system for a page turner. Electroencephalographic (EEG) signals are simultaneously retrieved using an EEG cap and the LabVIEW™ was used to perform signal pre-processing, feature extraction and execution command. Off-line analysis in the BCI system utilized visual stimulation to induce the individual’s visual evoked potential and adopted signal detrend function, independent component analysis and Savitzky-Golay filter method were used to resolve the interference caused by the background noise. Subsequently, EEG features are then extracted and classified to determine the command for the page turner system. The evaluation results from ten volunteers showed a recognition accuracy rate of 93.6 % and average time of around 4.95 s for controlling the page turner robot. The average information transfer rate was 49.10 bits/min. The study has successfully integrated LEGO™ components and a BCI system with high accuracy and good response time.
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