Abstract
Humans can easily learn how to use a new tool by using it repeatedly. It is called motor learning, and it has been reported that it involves specific brain activity. In this study, we investigated whether brain activity related to the learning process can be estimated by using functional near-infrared spectroscopy (fNIRS), which has advantages such as less of a constraint to movement. We compared two different models of the general linear model (GLM): the box learning model (BL model) and box learning + scalp blood flow model (BLS model). The results show that the BLS model considering the effect of scalp blood flow has higher validity than the BL model. In addition, the difference of brain activity between early and late learning phase was found. These results suggest the possibility that brain activity relating to motor learning can be evaluated using the proposed fNIRS-GLM model.
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Imai, T., Sato, T., Nambu, I., Wada, Y. (2012). Estimating Brain Activity of Motor Learning by Using fNIRS-GLM Analysis. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_48
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DOI: https://doi.org/10.1007/978-3-642-34475-6_48
Publisher Name: Springer, Berlin, Heidelberg
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