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.
Abstract. Lock-in amplifier is particularly important in the fNIRS-based system, because the lock-in amplifier can recover the low-level signals buried in significant amounts of noise. But the price of lock-in amplifier is very expensive. This paper presented a software method for designing digital lock-in amplifier. Compared with analogue lock-in amplifier, results show that software lock-in amplifier is feasible for experimental research and can replace the expensive analogue lock-in amplifier.
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