An adaboost-based weighting method for localizing human brain magnetic activity

This paper shows that pattern classification based on machine learning is a powerful tool for analyzing human brain activity data obtained by magnetoencephalography (MEG). In our previous work, a weighting method using multiple kernel learning was proposed, but this method had a high computational cost. In this paper, we propose a novel and fast weighting method using an AdaBoost algorithm to find the sensor area contributing to the accurate discrimination of vowels. Our AdaBoost simultaneously estimates both the classification boundary and the weight to each MEG sensor, with MEG amplitude obtained from each pair of sensors being an element of the feature vector. The estimated weight indicates how the corresponding sensor is useful for classifying the MEG response patterns. Our results for vowel recognition show the large-weight MEG sensors mainly in a language area of the brain and the high classification accuracy (91.0%) in the latency range between 50 and 150 ms.