Spatial localization of cortical time-frequency dynamics

被引：11

作者：

Dalal, Sarang S.

Guggisberg, Adrian G. ^{[3
]}

Edwards, Erik ^{[3
]}

Sekihara, Kensuke ^{[3
]}

Findlay, Anne M. ^{[3
]}

Canolty, Ryan T. ^{[1
,2
]}

Knight, Robert T. ^{[1
,2
]}

Barbaro, Nicholas M. ^{[3
]}

Kirsch, Heidi E. ^{[3
]}

Nagarajan, Srikantan S. ^{[3
]}

机构：

[1] Univ Calif Berkeley, Helen Wills Neurosci Inst, Berkeley, CA 94720 USA

[2] Univ Calif Berkeley, Dept Psychol, Berkeley, CA 94720 USA

[3] Univ Calif San Francisco, San Francisco, CA 94143 USA

来源：

2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16 | 2007年

关键词：

D O I：

10.1109/IEMBS.2007.4353449

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. We present a novel adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12-30 Hz) and a high gamma band (65-90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. We also revealed observed high gamma activity in the cerebellum. The proposed algorithm is highly parallelizable and runs efficiently on modern high performance computing clusters. This method enables noninvasive five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics.

引用

页码：4941 / +

页数：2

共 21 条

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