Drowsiness detection using combined neuroimaging: Overview and Challenges

TL;DR

This paper reviews the use of combined neuroimaging techniques like EEG+fNIRS and EEG+fMRI in brain-computer interfaces for detecting drowsiness, highlighting recent advances and challenges in this emerging research area.

Contribution

It provides the first comprehensive overview of combined neuroimaging-based BCIs specifically applied to drowsiness detection, summarizing recent developments and identifying challenges.

Findings

Combined neuroimaging enhances drowsiness detection accuracy.

Recent studies demonstrate feasibility of EEG+fNIRS and EEG+fMRI in sleep inertia detection.

Overview highlights key challenges in implementing these techniques in real-world settings.

Abstract

Brain-computer interfaces (BCIs) collect, analyze, and convert brain activity into instructions and send it to the detection system. BCI is becoming popular in under-brain activities in certain conditions such as attention-based tasks. Researchers have recently used combined neuroimaging techniques such as EEG+fNIRS and EEG+fMRI to solve many real-world problems. Drowsiness detection or sleep inertia is one of the central research areas for the combined neuroimaging techniques. This paper aims to investigate the recent application of combined neuroimaging-based BCI on drowsiness detection or sleep inertia. To this end, this is the only overview paper of the combined neuroimaging-based drowsiness detection system.