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Front Syst Neurosci. 2015 Feb 17;9:11. doi: 10.3389/fnsys.2015.00011. eCollection 2015.

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.

Author information

  • 1Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka University Osaka, Japan ; Multisensory Cognition and Computation Laboratory Universal Communication Research Institute, National Institute of Information and Communications Technology Kyoto, Japan.
  • 2Centre Aéronautique et Spatial, Institut Supérieur de l'Aéronautique et de l'Espace, Université de Toulouse Toulouse, France.
  • 3Multisensory Cognition and Computation Laboratory Universal Communication Research Institute, National Institute of Information and Communications Technology Kyoto, Japan.

Abstract

Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

KEYWORDS:

EEG; Kalman filter; auditory evoked response; brain machine interface; classification; dry EEG; independent component analysis; single trial

PMID:
25741249
PMCID:
PMC4330719
DOI:
10.3389/fnsys.2015.00011
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