关键词: 脑磁图（MEG）, 分类算法, 协方差矩阵, 黎曼距离, 巴氏距离

Abstract:

This paper proposes a new algorithm on MEG (magnetoencephalography) signals to classify MEG signals generated when human brain confronts the stimulation of different visual objects. At first, filtered MEG signals are projected to a new feature space and they will generate new features. The covariance features of new features, on behalf of the MEG signals features, will be used to presort samples in a vector space named tangent space. Then, the second time labeling results can be derived after adjusting prelabeled samples using Bhattacharyya distance. Finally, the ultimate classification results can be got by applying the Riemann distance method to adjust covariance matrices in manifold. The extensive experiments show that the combination of supervised and unsupervised algorithms can remarkably improve the classification accuracy of MEG signals.

Key words: magnetoencephalography (MEG), classification algorithm, covariance matrices, Riemann distance, Bhattacharyya distance