An adaboost-based weighting method for localizing human brain magnetic activity
An adaboost-based weighting method for localizing human brain magnetic activity
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发表时间：
2012-12
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通讯作者：
T. Takiguchi;R. Takashima;Y. Ariki;T. Imada;Jo-Fu Lotus Lin;P. Kuhl;M. Kawakatsu;M. Kotani
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作者：
T. Takiguchi;R. Takashima;Y. Ariki;T. Imada;Jo-Fu Lotus Lin;P. Kuhl;M. Kawakatsu;M. Kotani
本文表明，基于机器学习的模式分类是分析脑磁图（MEG）获得的人脑活动数据的有力工具。在我们以前的工作中，提出了一种使用多核学习的加权方法，但这种方法的计算成本很高。在本文中，我们提出了一种新的和快速的加权方法，使用AdaBoost算法找到的传感器区域有助于准确区分元音。我们的AdaBoost同时估计每个MEG传感器的分类边界和权重，从每对传感器获得的MEG幅度是特征向量的一个元素。估计的权重指示对应的传感器如何用于分类MEG响应模式。我们的研究结果显示，元音识别的大重量MEG传感器主要在大脑的语言区域和高的分类准确率（91.0%）的潜伏期范围内50和150毫秒。
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.