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

Saad Arif¹, Muhammad Jawad Khan^1,2, Noman Naseer³

¹School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad, Pakistan.

Frontiers in Human Neuroscience

|May 17, 2021

Summary

This summary is machine-generated.

This study introduces a passive brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS) for early drowsiness detection in drivers. The system achieved high accuracy, offering a promising solution for road safety.

Keywords:

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

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Published on: October 6, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Drowsiness during driving is a major cause of accidents.
Existing detection methods have limitations in real-time application.
Brain-computer interfaces (BCIs) offer a potential non-invasive monitoring solution.

Purpose of the Study:

To develop and validate a passive BCI system for early detection of driver drowsiness.
To utilize functional near-infrared spectroscopy (fNIRS) for measuring hemodynamic brain signals.
To assess the effectiveness of Vector Phase Analysis (VPA) for classifying drowsiness states.

Main Methods:

Acquired fNIRS signals from the right dorsolateral prefrontal cortex (DPFC) of 13 subjects during simulated driving.
Employed a continuous-wave fNIRS system with eight channels.
Utilized Vector Phase Analysis (VPA) with statistical and VPA-derived features for classification, alongside sleep stage-based thresholds.

Main Results:

Achieved average accuracies between 90.9% and 92.5% across five different classifiers.
The ensemble classifier with trajectory slopes of CORE vector magnitude and angle demonstrated the highest accuracy (95.3%) with a computation time of 40 ms.
Results were statistically significant (p < 0.05), indicating the reliability of the drowsiness detection.

Conclusions:

The proposed passive BCI system using fNIRS and VPA is a promising technique for online drowsiness detection.
The method allows for subject adaptation without recalibration, enhancing practical usability.
This technology holds potential for improving road safety by providing early warnings of driver fatigue.