Abstract
Optical topography (OT) signals measured during an experiment using activation tasks for certain brain functions contain neuronal-activation-induced blood oxygenation changes and also physiological changes. We used independent component analysis (ICA) for separating them, and extracted components related to brain activation without using any hemodynamic models. The analysis procedure has three stages: first, OT signals are separated into independent components (ICs) by using time-delayed decorrelation algorithm. Second, task-related ICs (TR-ICs) are selected from the separated ICs based on their mean inter-trial cross-correlations. Third, the TR-ICs are categorized into two clusters by k-mean clustering method and these are classified into TR activation-related ICs (TR-AICs) and TR noise ICs (TR-NICs). We applied this procedure to analysis of the OT signals obtained from experiments with one-handed finger tapping tasks. In the averaged waveform of TR-AICs, a small overshoot can be seen a few seconds after the onsets of each task and a few seconds after the ends, while the averaged waveform of TR-NICs show an N-shaped pattern.
