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Updated: Oct 11, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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Drowsiness detection using portable wireless EEG.

Sagila Gangadharan K¹, A P Vinod²

¹Department of Electrical Engineering, Indian Institute of Technology Palakkad, Palakkad, India.

Computer Methods and Programs in Biomedicine

|December 3, 2021

Summary

This summary is machine-generated.

This study demonstrates the feasibility of using a wearable Electroencephalogram (EEG) device for drowsiness detection. A single EEG electrode behind the ear achieved 78.3% accuracy in distinguishing alert from drowsy states.

Keywords:

Cross subject validation Drowsiness detection Electroencephalogram Heart rate correlation Support vector machine

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Recording Brain Activity with Ear-Electroencephalography

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Published on: March 31, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Wearable Technology

Background:

Drowsiness contributes significantly to traffic and workplace accidents.
Monitoring drowsiness is crucial for safety-critical applications like driving and air traffic control.
Wearable, lightweight, wireless Electroencephalogram (EEG)-based systems offer a potential solution for drowsiness detection.

Purpose of the Study:

To assess the feasibility of a consumer-grade, wearable EEG system for drowsiness detection.
To identify optimal EEG features for differentiating between alert and drowsy states.
To investigate the correlation between EEG features and heart rate during drowsiness.

Main Methods:

Extracted informative features from short daytime nap EEG signals.
Utilized Support Vector Machine (SVM) classifier with cross-subject validation.
Recorded concurrent EEG and heart rate data to study correlations.

Main Results:

Achieved 78.3% accuracy in classifying alert and drowsy states using selected EEG features.
EEG features from temporal electrodes were more significant for drowsiness detection than frontal electrodes.
Temporal electrode EEG features showed higher correlation with heart rate.

Conclusions:

Drowsiness detection is feasible using a single EEG electrode placed behind the ear.
The proposed algorithm demonstrates the potential of consumer-grade EEG for vigilance monitoring.
Temporal EEG features are highly indicative of drowsiness and correlate with physiological changes.