A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Independent component analysis for noisy data: MEG data analysis
Neural Networks
Digital Signal Processing (4th Edition)
Digital Signal Processing (4th Edition)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Effectiveness of Intrinsically Motivated Adaptive Agent for Sustainable Human-Agent Interaction
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
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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.