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. 2020 Feb 14:14:6.
doi: 10.3389/fninf.2020.00006. eCollection 2020.

Prediction of Pilot's Reaction Time Based on EEG Signals

Bartosz Binias  1 , Dariusz Myszor  2 , Henryk Palus  1 , Krzysztof A Cyran  3
Affiliations

Prediction of Pilot's Reaction Time Based on EEG Signals

Bartosz Binias et al. Front Neuroinform. .

Abstract

The main hypothesis of this work is that the time of delay in reaction to an unexpected event can be predicted on the basis of the brain activity recorded prior to that event. Such mental activity can be represented by electroencephalographic data. To test this hypothesis, we conducted a novel experiment involving 19 participants that took part in a 2-h long session of simulated aircraft flights. An EEG signal processing pipeline is proposed that consists of signal preprocessing, extracting bandpass features, and using regression to predict the reaction times. The prediction algorithms that are used in this study are the Least Absolute Shrinkage Operator and its Least Angle Regression modification, as well as Kernel Ridge and Radial Basis Support Vector Machine regression. The average Mean Absolute Error obtained across the 19 subjects was 114 ms. The present study demonstrates, for the first time, that it is possible to predict reaction times on the basis of EEG data. The presented solution can serve as a foundation for a system that can, in the future, increase the safety of air traffic.

Keywords: aircraft control human factors; cognitive workload; data mining; electroencephalography; prediction; reaction time; regression; safety.

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Figures

Figure 1
Figure 1
Positions of electrodes in the standard 10-10 electrode montage system. Highlighted locations reflect positioning of the Emotiv Epoc+ electrode with respect to 10-10 system-based on Koessler et al. (2009).
Figure 2
Figure 2
An illustrative representation of the EEG signal's TSI. The delay of response is calculated as the offset between the moment in time when the cue was presented to the subject and the moment when the subject's reaction to that cue was recorded. The prediction was made using only the segments of recorded EEG signal that immediately preceded the cue onset- or the “Temporal Segment of Interest” (TSI).
Figure 3
Figure 3
EEG signal processing pipeline (for single electrode).
Figure 4
Figure 4
Histogram of cumulative feature selections for all subjects for SVMRBF algorithms. Only features selected more than once were included.
See this image and copyright information in PMC

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