有哪些信誉好的足球投注网站decoding semi-constrained brain activity from fmri using support vector machines and gaussian processes解码大脑活动semi-constrained使用支持向量机从功能磁共振成像和高斯过程.pdfVIP
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decoding semi-constrained brain activity from fmri using support vector machines and gaussian processes解码大脑活动semi-constrained使用支持向量机从功能磁共振成像和高斯过程
Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes 1,2 . ´ 1. 2,3 1,4 1,2 Jessica Schrouff * , Caroline Kusse , Louis Wehenkel , Pierre Maquet , Christophe Phillips ` ` ` ` 1 Cyclotron Research Centre, University of Liege, Liege, Belgium, 2 Department of Electrical Engineering and Computer Science, University of Liege, Liege, Belgium, ` ` ` ` 3 Giga-R, Systems Biology and Chemical Biology, University of Liege, Liege, Belgium, 4 Department of Neurology, Liege University Hospital, Liege, Belgium Abstract Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support
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