The Effect of Muscle Artifact Reduction Methods on Few-channel SSVEPs during Head Movements.

Brain-computer interfaces (BCIs) with steady-state visual evoked potentials (SSVEPs) caused by flickering stimuli have caught attention as communication tools between human brains and external machines through a head-mounted display (HMD). When applying SSVEP-based BCIs to real-life environments, the head must be moved to watch the stimuli displayed in an HMD, which generates muscular artifacts and significantly reduces BCI performance. In this study, we examined four-class SSVEP identification accuracies by using four artifact reduction methods in the situation of moving the head for both simulation and real datasets. In the simulation dataset, we found that artifact subspace reconstruction (ASR) and multi-scale dictionary learning (MSDL) showed better results especially at low signal-to-noise ratio. In the real dataset, we observed that reducing muscular artifacts resulted in performance degradation for independent component analysis-based methods, while ASR and MSDL showed relatively limited degradation and in some cases improved performance. Our future work is to improve ASR and MSDL for high performance with real data and to apply them to an online SSVEP-based BCI where the user moves his/her head.

Estimating asymptotic phase and amplitude functions of limit-cycle oscillators from time series data.

We propose a method for estimating the asymptotic phase and amplitude functions of limit-cycle oscillators using observed time series data without prior knowledge of their dynamical equations. The estimation is performed by polynomial regression and can be solved as a convex optimization problem. The validity of the proposed method is numerically illustrated by using two-dimensional limit-cycle oscillators as examples. As an application, we demonstrate data-driven fast entrainment with amplitude suppression using the optimal periodic input derived from the estimated phase and amplitude functions.