Journal ArticleDOI
Development of Wireless Brain Computer Interface With Embedded Multitask Scheduling and its Application on Real-Time Driver's Drowsiness Detection and Warning
Chin-Teng Lin,Yu-Chieh Chen,Teng-Yi Huang,Tien-Ting Chiu,Li-Wei Ko,Sheng-Fu Liang,Hung-Yi Hsieh,Shang Hwa Hsu,Jeng Ren Duann +8 more
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.read more
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
Scott Makeig,Klaus Gramann,Tzyy-Ping Jung,Terrence J. Sejnowski,Terrence J. Sejnowski,Howard Poizner +5 more
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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