Abstract
A passive brain–computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects’ data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.
Highlights
Invasive and noninvasive techniques are used in brain–computer interface (BCI) for the detection and measurement of brain activities using different BCI modalities (Sun et al, 2020; Tortora et al, 2020)
While inner threshold circles are based on mean cerebral oxygen regulation (CORE) vector magnitude of non-rapid eye movement (NREM) sleep stages N1, N2, and
Drowsiness activity is detected when the Vector phase analysis (VPA) trajectory crosses the W state threshold circle in phases 7 and 8 of the vector phase diagram according to the criterion of Eqs. (11, 12), showing a decrease in COE, which is an indicator of transition from W to NREM sleep
Summary
Invasive and noninvasive techniques are used in brain–computer interface (BCI) for the detection and measurement of brain activities using different BCI modalities (Sun et al, 2020; Tortora et al, 2020). Invasive BCI is based upon placing electrodes inside the brain cortex under direct interaction with neurons and requires complex surgery, medical conditions, and greater risk of infections (Yoo et al, 2018; Alkawadri, 2019; Romanelli et al, 2019). In ECoG, the electrode array is placed inside the skull and directly above the cortex. It requires easier surgery, and medical conditions like the infectious risk are very less (Romanelli et al, 2019). Medical conditions like the infectious risk are very less (Romanelli et al, 2019) It provides the best signal quality, and good temporal and spatial resolution (Volkova et al, 2019). Noninvasive BCIs are more commonly used due to no surgery requirements and the absence of medical risks (Sosnik and Ben Zur, 2020)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
