Empirische Inferenz Article 2008

Voluntary Brain Regulation and Communication with ECoG-Signals

Brain–computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.

Author(s): Hinterberger, T. and Widmann, G. and Lal, TN. and Hill, J. and Tangermann, M. and Rosenstiel, W. and Schölkopf, B. and Elger, CE. and Birbaumer, N.
Journal: Epilepsy and Behavior
Volume: 13
Number (issue): 2
Pages: 300-306
Year: 2008
Month: August
Day: 0
Bibtex Type: Article (article)
DOI: 10.1016/j.yebeh.2008.03.014
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex

@article{5085,
  title = {Voluntary Brain Regulation and Communication with ECoG-Signals},
  journal = {Epilepsy and Behavior},
  abstract = {Brain–computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.},
  volume = {13},
  number = {2},
  pages = {300-306},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2008},
  slug = {5085},
  author = {Hinterberger, T. and Widmann, G. and Lal, TN. and Hill, J. and Tangermann, M. and Rosenstiel, W. and Sch{\"o}lkopf, B. and Elger, CE. and Birbaumer, N.},
  month_numeric = {8}
}