A new multiple-kernel-learning weighting method for localizing human brain magnetic activity
Tetsuya Takiguchi, Toshiaki Imada, Ryoichi Takashima, Yasuo Ariki, Jing Fung Lin, Patricia K. Kuhl, Masaki Kawakatsu, M. Kotani · 2012
This paper shows that pattern classification based on machine learning is a powerful tool to analyze human brain activity data obtained by magnetoencephalography (MEG). We propose a new weighting method using a multiple kernel learning (MKL) algorithm to localize the brain area contributing to the accurate vowel discrimination. Our MKL simultaneously estimates both the classification boundary and the weight of each MEG sensor; MEG amplitude obtained from each pair of sensors is an element of the feature vector. The estimated weight indicates how the corresponding sensor is useful for classifying the MEG response patterns. Our results show both the large-weight MEG sensors mainly in a language area of the brain and the high classification accuracy (73.0%) in the 100 ~ 200 ms latency range.