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

Callan, Daniel E., Durantin, Gautier and Terzibas, Cengiz (2015) Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight. Frontiers in Systems Neuroscience, 9 1-12. doi:10.3389/fnsys.2015.00011


Author Callan, Daniel E.
Durantin, Gautier
Terzibas, Cengiz
Title Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight
Journal name Frontiers in Systems Neuroscience   Check publisher's open access policy
ISSN 1662-5137
Publication date 2015-02-17
Year available 2015
Sub-type Article (original research)
DOI 10.3389/fnsys.2015.00011
Open Access Status DOI
Volume 9
Start page 1
End page 12
Total pages 12
Place of publication Lausanne, Switzerland
Publisher Frontiers Research Foundation
Collection year 2016
Language eng
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.
Keyword Auditory evoked response
Brain machine interface
Classification
Dry EEG
EEG
Independent component analysis
Kalman filter
Single trial
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
 
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