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Related Experiment Video

Updated: Mar 31, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Passive BCI based on drowsiness detection: an fNIRS study.

M Jawad Khan¹, Keum-Shik Hong²

¹School of Mechanical Engineering, Pusan National University; 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, South Korea.

Biomedical Optics Express

|October 28, 2015

Summary

This summary is machine-generated.

Functional near-infrared spectroscopy (fNIRS) effectively detects drowsiness in passive brain-computer interfaces (BCI). This brain monitoring method shows high accuracy in identifying drowsy states for enhanced safety.

Keywords:

(070.5010) Pattern recognition (170.2655) Functional monitoring and imaging (200.3050) Information processing (300.0300) Spectroscopy

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Last Updated: Mar 31, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

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Published on: August 8, 2019

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

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fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Drowsiness poses significant risks, particularly in tasks like driving.
Passive brain-computer interfaces (BCI) offer a non-invasive method for monitoring cognitive states.
Functional near-infrared spectroscopy (fNIRS) is a promising technique for measuring brain activity.

Purpose of the Study:

To discriminate between alert and drowsy states using fNIRS signals.
To evaluate the efficacy of a passive BCI for drowsiness detection.
To identify optimal features and brain regions for accurate drowsiness classification.

Main Methods:

Utilized continuous-wave fNIRS to record brain activity from 13 healthy subjects in a driving simulator.
Employed Linear Discriminant Analysis (LDA) for classification.
Analyzed features including mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak across different time windows (0-5, 0-10, 0-15 seconds).

Main Results:

The best classification performance was achieved using mean oxyhemoglobin, signal peak, and sum of peaks as features.
High average accuracies were obtained in the right dorsolateral prefrontal cortex, reaching up to 84.9% in the 15-second window.
The proposed method demonstrated effective utility for detecting drowsiness.

Conclusions:

fNIRS-based passive BCIs can reliably detect drowsiness.
Specific brain regions (prefrontal cortex) and signal features are crucial for accurate drowsiness detection.
This technology holds potential for enhancing safety in critical tasks by monitoring driver alertness.