Independent Component Analysis of Multi-channel Near-Infrared Spectroscopic Signals by Time-Delayed Decorrelation
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- Sano T., Matsuzaki S., Wada Y. (2010) Independent Component Analysis of Multi-channel Near-Infrared Spectroscopic Signals by Time-Delayed Decorrelation. In: Diamantaras K., Duch W., Iliadis L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg
Multi-channel near-infrared spectroscopy (NIRS) is increasingly used in empirical studies monitoring human brain activity. In a recent study, an independent component analysis (ICA) technique using time-delayed decorrelation was applied to NIRS signals since those signals reflect cerebral blood flow changes caused by task-induced responses as well as various artifacts. The decorrelation technique is important in NIRS-based analyses and may facilitate accurate separation of independent signals generated by oxygenated/deoxygenated hemoglobin concentration changes. We introduce an algorithm using time-delayed correlations that enable estimation of independent components (ICs) in which the number of components is fewer than that of observed sources; the conventional approach using a larger number of components may deteriorate settling of the solution. In a simulation, the algorithm was shown capable of estimating the number of ICs of virtually observed signals set by an experimenter, with the simulation reproducing seven sources where each was a mixture of three ICs and white noises. In addition, the algorithm was introduced in an experiment using ICs of NIRS signals observed during finger-tapping movements. Experimental results showed consistency and reproducibility of the estimated ICs that are attributed to patterns in the spatial distribution and temporal structure.
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