Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis

Gomez Rodriguez, M; Grosse-Wentrup, M; Peters, J; Naros, G; Hill, J; Schölkopf, B; Gharabaghi, A

doi:10.1109/WBD.2010.17

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Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis

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Gomez Rodriguez, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Grosse-Wentrup, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Peters, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Hill, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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引用

Gomez Rodriguez, M., Grosse-Wentrup, M., Peters, J., Naros, G., Hill, J., Schölkopf, B., & Gharabaghi, A. (2010). Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis. In J., Richiardi, D., van de Ville, C., Davatzikos, & J., Mourao-Miranda (Eds.), 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (ICPR WBD 2010) (pp. 36-39). Piscataway, NJ, USA: IEEE.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-BEB8-5

要旨

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.