» Articles » PMID: 18440904

Development of Wireless Brain Computer Interface with Embedded Multitask Scheduling and Its Application on Real-time Driver's Drowsiness Detection and Warning

Overview
Date 2008 Apr 29
PMID 18440904
Citations 23
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

In-Car Environment Control Using an SSVEP-Based Brain-Computer Interface with Visual Stimuli Presented on Head-Up Display: Performance Comparison with a Button-Press Interface.

Park S, Kim M, Nam H, Kwon J, Im C Sensors (Basel). 2024; 24(2).

PMID: 38257638 PMC: 10819861. DOI: 10.3390/s24020545.


Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Lin C, Wang Y, Chen S, Huang K, Liao L Med Biol Eng Comput. 2023; 61(11):3003-3019.

PMID: 37563528 DOI: 10.1007/s11517-023-02879-y.


A Novel Quick-Response Eigenface Analysis Scheme for Brain-Computer Interfaces.

Choi H, Park J, Yang Y Sensors (Basel). 2022; 22(15).

PMID: 35957420 PMC: 9370919. DOI: 10.3390/s22155860.


Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect.

Chen J, Yan M, Zhu F, Xu J, Li H, Sun X Sensors (Basel). 2022; 22(13).

PMID: 35808213 PMC: 9269348. DOI: 10.3390/s22134717.


Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review.

Li G, Chung W Sensors (Basel). 2022; 22(3).

PMID: 35161844 PMC: 8840041. DOI: 10.3390/s22031100.