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
First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (WBD) 2010
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.