Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2023 Mar 11

PMID 36904587

Authors

Khanh Ha Nguyen

Matthew Ebbatson

Yvonne Tran

Ashley Craig

Hung Nguyen

Rifai Chai

Affiliations

Soon will be listed here.

Abstract

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

Citing Articles

A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks.

Ardabili S, Bahmani S, Zare Lahijan L, Khaleghi N, Sheykhivand S, Danishvar S Sensors (Basel). 2024; 24(2).

PMID: 38257457 PMC: 10819416. DOI: 10.3390/s24020364.

EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism.

Wang L, Song F, Zhou T, Hao J, Ryu K Sensors (Basel). 2023; 23(20).

PMID: 37896480 PMC: 10611368. DOI: 10.3390/s23208386.

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