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Background: Each year millions of vehicles suffer crashes on the roads globally due to
deteriorated mental state of the drivers during driving tasks which result in higher casualties.
Drowsy driving is the leading cause of high fatality rate which is instigated due to sleep
deprivation, fatigue, and anxiety, etc. Vehicular, and driver’s behavioral data-dependent
systems detect the drowsiness after its onset when an accident is more likely, and they are also
subject to false identification. The drowsy mental state of drivers must be detected earlier for
in-time warning to avoid fatal losses, and also the system must be less intrusive, and adaptable
for normal driving tasks. Detection systems using physiological signals from human organs are
comparatively underexplored in which bodily states of the subject can be identified earlier and
with more reliability. It is postulated that all the bodily states are primarily originated from the
human brain which could be a promising region to detect drowsiness at an earlier stage. Among
many physiological signals, electroencephalography (EEG), and functional near-infrared
spectroscopy (fNIRS) are chosen for this study because they are proved to be more portable,
easy to use, non-invasive, and reliable brain modalities for human mental state detection.
Aim and Objectives: This study aimed to design a passive brain-computer interface (pBCI)
scheme for the earlier detection of driver drowsiness with minimum intrusion into the driving
task. Design objectives for the pBCI scheme were to select the channel of interest (COI), online
detection with a shorter time window, minimum recalibration and setup time, and development
of a widely applicable standard as an inter-subject transfer framework (iSTF) to cater to the
inter-subject variability. All these objectives lead to such a pBCI system that is readily available
for any subject, anytime, easy to use with minimal design, and yet detecting the drowsiness
correctly at an earlier stage to avoid life losses.
Methodology: Multichannel EEG and fNIRS brain signals from anterior, posterior, and lateral
brain regions of sleep-deprived drowsy subjects were acquired during the simulated driving
task for post hoc analysis. Initial pBCIschemes used labeled EEG data acquired from prefrontal
(PFC), frontal, and occipital cortices for extracting the eight spectral, and eight temporal
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features of EEG signals. Seven supervised machine learning classifiers were used to do the
cross-validated binary classification of drowsy, and alert brain states. Initial design only
achieved a few objectives and generated the need to use different modalities to meet all the
requirements. The final pBCI scheme used labeled fNIRS data acquired from PFC and
dorsolateral prefrontal cortices (DLPFC) for extracting the six cerebral oxygen regulation
(CORE) and three hemodynamic signal features. CORE states of wakefulness and non-rapid
eye movement (NREM) sleep stages were used to design a novel standard framework for wide
applicability, and sleep stage classification using the vector phase analysis (VPA) approach.
VPA was used for classifying microsleep/lapse, and drowsiness detection was done using the
proposed brain hemodynamic patterns.
Results: 𝛿, 𝜃, 𝛼, 𝛽 band powers as EEG spectral features achieved 82% accuracy in 10 s
detection window. Signal skewness, variance, mean, peak as EEG temporal features achieved
87.2% accuracy in 1 s detection window. Ensemble classifier declared F8 as COI for earlier
drowsiness detection using both the EEG pBCI schemes. Only the objectives of the short
detection window and COI were achieved with the initial designs. In the final design with
fNIRS, the novel VPA features: CORE vector gradients, achieved 94.1% accuracy in 5 s
detection window for NREM sleep stage classification using ensemble classifier with the least
computation time of 44 ms. Precise spatial localization of fNIRS declared AF8 position in right
DLPFC as COI. The novel sleep stages-based threshold criteria along with VPA were crossvalidated as a standard iSTF for online microsleep detection with the least recalibration and
setup time. Feature selection and achieved results were validated with various statistical
significance tests. All the design objectives were attained with the fNIRS-based pBCI scheme.
Conclusion: The aim to detect the driver's drowsiness earlier with minimum intrusion into the
driving task, is accomplished. The presented fNIRS-based adaptive pBCI scheme is readily
available for any subject, anytime, easy to wear with minimal ergonomic design, capable of
real-time, correct, and early detection of the driver drowsiness to lessen the life losses in
vehicular driving scenarios. The recommended research directions will surely justify, improve,
and broaden the application horizons of the presented design