Decoding magnetoencephalographic rhythmic activity using spectrospatial information

被引:16
作者
Kauppi, Jukka-Pekka [1 ,2 ,3 ]
Parkkonen, Lauri [3 ,4 ]
Hari, Riitta [3 ]
Hyvarinen, Aapo [1 ,2 ,5 ]
机构
[1] Univ Helsinki, Dept Comp Sci, SF-00510 Helsinki, Finland
[2] Univ Helsinki, HIIT, Helsinki, Finland
[3] Aalto Univ, Sch Sci, OV Lounasmaa Lab, Brain Res Unit, Espoo, Finland
[4] Aalto Univ, Sch Sci, Dept Biomed Engn & Computat Sci, Espoo, Finland
[5] Univ Helsinki, Dept Math & Stat, Helsinki, Finland
基金
欧洲研究理事会; 芬兰科学院;
关键词
Decoding; Magnetoencephalography; Rhythmic activity; Time-frequency analysis; Linear discriminant analysis; Independent component analysis; INDEPENDENT COMPONENT ANALYSIS; BRAIN-COMPUTER INTERFACES; SINGLE-TRIAL ANALYSIS; HUMAN VISUAL-CORTEX; LOGISTIC-REGRESSION; VARIABLE SELECTION; CORTICAL ACTIVITY; FMRI ACTIVITY; ELASTIC NET; CLASSIFICATION;
D O I
10.1016/j.neuroimage.2013.07.026
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We propose a new data-driven decoding method called Spectral Linear Discriminant Analysis (Spectral LDA) for the analysis of magnetoencephalography (MEG). The method allows investigation of changes in rhythmic neural activity as a result of different stimuli and tasks. The introduced classification model only assumes that each "brain state" can be characterized as a combination of neural sources, each of which shows rhythmic activity at one or several frequency bands. Furthermore, the model allows the oscillation frequencies to be different for each such state. We present decoding results from 9 subjects in a four-category classification problem defined by an experiment involving randomly alternating epochs of auditory, visual and tactile stimuli interspersed with rest periods. The performance of Spectral LDA was very competitive compared with four alternative classifiers based on different assumptions concerning the organization of rhythmic brain activity. In addition, the spectral and spatial patterns extracted automatically on the basis of trained classifiers showed that Spectral LDA offers a novel and interesting way of analyzing spectrospatial oscillatory neural activity across the brain. All the presented classification methods and visualization tools are freely available as a Matlab toolbox. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:921 / 936
页数:16
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