Development of Wireless Brain Computer Interface With Embedded Multitask Scheduling and its Application on Real-Time Driver's Drowsiness Detection and Warning

Abstract

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

Forward Collision Probability Evaluation Based on Tired Index of Driver

“…There are many researchers who focus on the drowsiness of drivers [20][21][22][23][24][25][26][27][28]. In this paper, we use the EEG activities for determining drowsy of driver.…”

Review of Wireless and Wearable Electroencephalogram Systems and Brain-Computer Interfaces – A Mini-Review

“…Their study showed that users felt, after some training, more natural to interact with the BCI. Table 2 summarizes the BCI systems mentioned above [1,2,8,25,26,31,32] . Except for the BCI system developed by Cincotti et al [26] , whose wireless transmission lies between the host PC (intermediate stage) and the vibrotactile feedback device (back end), other BCI systems employed a wireless transmission module between the signal acquisition (front end) and signal analysis units (intermediate stage).…”

“…Given the recent development of embedded system and signal processing techniques, it seems practical to implement sophisticated algorithms in embedded systems for online BCI systems. Recently, Lin et al [31,32] developed and demonstrated a wearable and wireless EEG acquisition and real-time data analysis module based on an embedded system for maximizing the usability, portability and wearability of BCI. The details are presented in the next section.…”

Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning

“…Hence, it is significant to develop the automatic system to liberate the neurologists from long-term EEG interpretation. The goal of this research is to develop an efficient system to detect drowsy/ alert epochs which will be applied in: (1) assistance of neurologists to review potential segments of alert and drowsy states for further http inspection (Nakamura et al, 2005;Shibasaki et al, 2014); (2) monitoring and warning systems for detecting the drowsy states to prevent the accumulation of mental fatigue and declines of work efficiency in real-world operation and living environments (Lin et al, 2008).…”