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Journal ArticleDOI

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

TLDR
A novel brain-computer interface 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 is proposed.
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.

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Citations
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Journal ArticleDOI

Driver Inattention Monitoring System for Intelligent Vehicles: A Review

TL;DR: The hybrid measures are believed to give more reliable solutions compared with single driver physical measures or driving performance measures, because the hybrid measures minimize the number of false alarms and maintain a high recognition rate, which promote the acceptance of the system.
Journal ArticleDOI

How about taking a low-cost, small, and wireless EEG for a walk?

TL;DR: It is concluded that good quality, single-trial EEG data suitable for mobile brain-computer interfaces can be obtained with affordable hardware.
Journal ArticleDOI

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

TL;DR: Brain-machine interfaces research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema.
Journal ArticleDOI

Linking brain, mind and behavior

TL;DR: Wearable mobile brain/body imaging systems that continuously capture the wearer's high-density electrical brain and muscle signals, three-dimensional body movements, audiovisual scene and point of regard, plus new data-driven analysis methods to model their interrelationships are proposed.
Journal ArticleDOI

Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement

TL;DR: A novel dry foam-based electrode, fabricated by electrically conductive polymer foam covered by a conductive fabric, performs better for long-term EEG measurement, and is practicable for daily life applications.
References
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Journal ArticleDOI

Design and implementation of a brain-computer interface with high transfer rates

TL;DR: A brain-computer interface that can help users to input phone numbers based on the steady-state visual evoked potential (SSVEP), which has noninvasive signal recording, little training required for use, and high information transfer rate.
Journal ArticleDOI

A BCI-based environmental controller for the motion-disabled

TL;DR: An environmental controller using a BCI technique based on steady-state visual evoked potential composed of a stimulator, a digital signal processor, and a trainable infrared remote-controller that has been applied to the control of an electric apparatus successfully.
Journal ArticleDOI

EEG-based drowsiness estimation for safety driving using independent component analysis

TL;DR: In this paper, the authors developed a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator.
Journal ArticleDOI

Multichannel EEG-based brain-computer communication.

TL;DR: This study learned to use two channels of bipolar EEG activity to control 2-dimensional movement of a cursor on a computer screen using multichannel EEG-based communication to help those with severe motor disabilities.
Related Papers (5)
Trending Questions (1)
How to connect amplifier to Bluetooth module?

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.