PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis
M Gomez-Rodriguez, Moritz Grosse-Wentrup, Jan Peters, G Naros, Jeremy Hill, Bernhard Schölkopf and A Gharabaghi
(2010) First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD) 2010 . IEEE . ISBN 9781424484867

Abstract

Brain-Computer Interfaces (BCI) that rely upon epidural electrocorticographic signals may become a promising tool for neurorehabilitation of patients with severe hemiparatic syndromes due to cerebrovascular, traumatic or tumor-related brain damage. Here, we show in a patient-based feasibility study that online classification of arm movement intention is possible. The intention to move or to rest can be identified with high accuracy (~90%), which is sufficient for BCI-guided neurorehabilitation. The observed spatial distribution of relevant features on the motor cortex indicates that cortical reorganization has been induced by the brain lesion. Low and high-frequency components of the electrocorticographic power spectrum provide complementary information towards classification of arm movement intention.

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EPrint Type:Book
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:7846
Deposited By:Moritz Grosse-Wentrup
Deposited On:17 March 2011