Online Motion-Artifact Removal in fNIRS Signals: Combined Square-Root Cubature Kalman Filter and Weighted Moving Average Model Approach

Abstract

One of the advantages of fNIRS systems is their portability for real-world applications. Therefore, the online rejection of the motion artifacts in functional near-infrared spectroscopy (fNIRS) signals is one of the essential research topics. We proposed a square-root cubature Kalman filter using a weighted moving average model to tract the dynamics of motion artifacts. The rationale and feasibility of the method were discussed using simulated data generated using a Balloon model. The results show that the proposed filter has a good suppression of spike-like motion artifacts. Finally, conclusions and further study are discussed.