Comparison of Optical and Concentration Feature Used for fNIRS-Based BCI System Using HMM
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Abstract
Brain-Computer Interface (BCI) is very useful for people who lose limb control such as amyotrophic lateral sclerosis (ALS) patients, stroke patients and patients with prosthetic limbs. Among all the brain signal acquisition devices, functional near-infrared spectroscopy (fNIRS) is an efficient approach to detect hemodynamic responses correlated with brain activities using optical method, and its spatial resolution is much higher than EEG. In this paper, we investigate the classification performance of both optical signal and hemodynic signal that both used in fNIRS-based BCI system using Hidden Markov Model (HMM). Our results show that hemodynamic signal has a much lower error rate than optical signal, especially the Oxy-hemoglobin (HbO) has the lowest error rate. This result is important for researchers who want to design an fNIRS-based BCI system and get better performance.
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