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




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%. 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%). 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.

Author(s): Grosse-Wentrup, M. and Schölkopf, B.
Journal: Journal of Neural Engineering
Volume: 11
Number (issue): 5
Pages: 056015
Year: 2014
Day: 0

Department(s): Empirical Inference
Research Project(s): Brain-Computer Interfaces
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1088/1741-2560/11/5/056015

Links: Web


  title = {A Brain-Computer Interface Based on Self-Regulation of Gamma-Oscillations in the Superior Parietal Cortex},
  author = {Grosse-Wentrup, M. and Sch{\"o}lkopf, B.},
  journal = {Journal of Neural Engineering},
  volume = {11},
  number = {5},
  pages = {056015},
  year = {2014}