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Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis

MPG-Autoren
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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;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Naros G, 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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Zitation

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 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (ICPR WBD 2010) (pp. 36-39). Piscataway, NJ, USA: IEEE.


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