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.
Highlights
Technology capable of augmenting human performance by means of feedback of decoded neural states has potential for many types of neuroergonimic applications
The independent components showing auditory evoked potentials used to train the Least Squares Probabilistic Classification (LSPC) models were the following for the various conditions: Platform Off (2, 3), Platform On (1, 3), Biplane Engine Off (2, 3), Biplane Engine On (2, 16)
The results of the single trial classification performance for audio chirp verses silent stimuli for all environmental and analysis method conditions were significantly above chance (p < 0.05) based on permutation testing of 100 random shuffling of the labels compared to the mean performance of the respective models trained with correct labels
Summary
Technology capable of augmenting human performance by means of feedback of decoded neural states has potential for many types of neuroergonimic applications. Neuroergonomics is the study of the human brain in relation to performance at work, at home, in transportation, and in everyday settings with the goal of using this knowledge to design technologies and work environments to augment human behavior to enhance safety, usability, efficiency and enjoyment (Parasuraman, 2003; Parasuraman and Rizzo, 2008). Unlike the laboratory where brain recordings can be made under controlled conditions, in real world situations there is considerable additional physiological and environmental noise that must be dealt with. In addition it is likely the case that brain dynamics differ in real-world environments compared to those of the laboratory (McDowell et al, 2013; Lin et al, 2014)
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