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  A Brain-Computer Interface Based on Self-Regulation of Gamma-Oscillations in the Superior Parietal Cortex

Grosse-Wentrup, M., & Schölkopf, B. (2014). A Brain-Computer Interface Based on Self-Regulation of Gamma-Oscillations in the Superior Parietal Cortex. Journal of Neural Engineering, 11(5): 056015. doi:10.1088/1741-2560/11/5/056015.

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 Urheber:
Grosse-Wentrup, Moritz1, Autor           
Schölkopf, Bernhard1, Autor           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Schlagwörter: Abt. Schölkopf
 Zusammenfassung: Objective. Brain computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI. Approach. We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS. Main results. Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2 percent. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8 percent). Significance. Self-regulation of gamma-power in the SPC is a feasible paradigm for brain computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.

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 Datum: 20142014-10
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1088/1741-2560/11/5/056015
BibTex Citekey: GrosseWentrupS2014
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Titel: Journal of Neural Engineering
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Bristol, UK : IOP Publishing
Seiten: - Band / Heft: 11 (5) Artikelnummer: 056015 Start- / Endseite: - Identifikator: ISSN: 1741-2552
ISSN: 1741-2560