Real Time Vigilance Detection using Frontal EEG

Abstract

Vigilance of an operator is compromised in performing many monotonous activities like workshop and manufacturing floor tasks, driving, night shift workers, flying, and in general any activity which requires keen attention of an individual over prolonged periods of time. Driver or operator fatigue in these situations leads to drowsiness and lowered vigilance which is one of the largest contributors to injuries and fatalities amongst road accidents or workshop floor accidents. Having a vigilance monitoring system to detect drop in vigilance in these situations becomes very important. This paper presents a system which uses non-invasively recorded Frontal EEG from an easy-to-use commercially available Brain Computer Interface wearable device to determine the vigilance state of an individual. The change in the power spectrum in the Frontal Theta Band (4-8Hz) of an individual’s brain wave predicts the changes in the attention level of an individual - providing an early detection and warning system. This method provides an accurate, yet cheap and practical system for vigilance monitoring across different environments.

Optimal feature‐algorithm combination research for EEG fatigue driving detection based on functional brain network

“…In some scholars' driving fatigue research based on human physiological signals, the main objects are pulse beat, electrooculography (EOG), electromyography (EMG), electrocardiography (ECG), and electroencephalography (EEG). In terms of detection accuracy, direct use of driver physiological characteristics for fatigue driving state detection has the highest recognition accuracy and most directly responds to fatigue [19][20][21][22][23][24][25][26].…”

“…Because the brain is a complex network, and most of the above studies [4, 8–10, 19, 20] used too little EEG channel data from subjects, this does not accurately reflect the functional connections between the brains. Moreover, most of the above studies were trained using single or double features under a particular algorithm [22–24, 29].…”

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“…In the last few decades, we have seen many advances in computing technologies, both in hardware miniaturization, data communication, and software solutions, enabling a scenario for using "smart" devices embedded in the most diverse areas of daily life. Nowadays, many healthcare, energy grids, cities, transportation, agriculture, and industry domains use connected sensors, devices, and machines autonomously communicating via the Internet [1,2,3,4,5]. Each domain area has its particularities and constraints, demanding different resources while sensing, processing, transmitting and presenting data [6,7].…”