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

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Free keywords: Abt. Schölkopf
 Abstract: 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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 Dates: 20142014-10
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1088/1741-2560/11/5/056015
BibTex Citekey: GrosseWentrupS2014
 Degree: -

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Title: Journal of Neural Engineering
Source Genre: Journal
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Publ. Info: Bristol, UK : IOP Publishing
Pages: - Volume / Issue: 11 (5) Sequence Number: 056015 Start / End Page: - Identifier: ISSN: 1741-2552
ISSN: 1741-2560