Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface

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.

Keywords: brain-computer interface; cerebral oxygen regulation; drowsiness detection; feature selection; functional near-infrared spectroscopy; multiclass classification; sleep stages; vector phase analysis.

FIGURE 1

Experimental setup and its flowchart for drowsiness detection.

FIGURE 2

Optode placement at Region A (right dorsolateral prefrontal cortex), channels 1–8.

FIGURE 3

Vector phase diagram (Hong and Naseer, 2016).

FIGURE 4

Threshold circles of wakefulness and non-rapid eye movement (NREM) sleep stages employed for drowsiness detection and sleep stage classification, obtained from Eqs. (9, 10).

FIGURE 5

Vector phase analysis (VPA) trajectories of all channels (Subject 1) obtained by plotting Eqs. (7, 8) at the drowsiness stage. The shaded boxes show the active detection channels in which trajectory has crossed the W threshold circle in the fourth quadrant according to the magnitude and angle criterion.

FIGURE 6

Vector phase analysis (VPA) trajectories of various subjects at an active channel (Channel 8) showing drowsiness activity detection, located at F8 electrode position of the 10–20 system.

FIGURE 7

Thirty-six 4-class feature spaces combining all features [six vector phase analysis (VPA) and three statistical] for separating the W, N1, N2, and N3 stages represented by pink, blue, red, and black colored data points, respectively.

FIGURE 8

Average classification accuracies with variance bounds obtained by using different vector phase analysis (VPA) feature pairs.

FIGURE 9

Classification performance measures [Subject 9, all channels, m(|R|) vs. m(∠R) feature set, ensemble classifier]: (A) Confusion matrix with the number of observations at diagonal and off-diagonal entries, true-positive rate (TPR), and false-negative rate (FNR) at the right columns, and positive predictive value (PPV) and false discovery rate (FDR) at the bottom rows. (B) Multiclass receiver operating characteristic (ROC) curves and area under the curve (AUC) for all sleep stages.