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Due to its convenient, low physical restraint, and electric noise tolerant features, functional near-infrared spectroscopy (fNIRS) is expected to be a useful tool in monitoring users' brain activity in HCI. However, fNIRS measurement suffers from various kinds of artifacts, and no standardized method for artifact reduction has been established so far. In this study, we compared high-pass/band-pass filtering, global and local average references, independent component analysis (ICA) based method, and their combinations. Their effectiveness for artifact reduction was evaluated by a cognitive task recognition experiment. The results showed all the methods have artifact reduction capability, but their effectiveness depends on subjects and tasks. This suggests that it can be more practical to try various artifact reduction methods and chose the best one for each task and subject, instead of pursuing a single standardized method.

A Comparison of Artifact Reduction Methods for Real-Time Analysis of fNIRS Data