Browsing by Author "Chu, Tso-Yao"

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Design of Wearable Brain Computer Interface Based on Motor Imagery
(2014-01-01)
Lin, Chuan-Lung
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Chu, Tso-Yao
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Wu, Pei-Jung
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Wang, Chen-An
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Lin, Bor-Shyh
Brain computer interface (BCI) is a communication system to establish a communication interface between the brain and the device. Motor imagery-based brain computer interface is one of asynchronous BCIs, and have been widely developed in recent years. However, most of motor imagery-based BCI require bulk EEG machines, and lots of EEG data have to be transmitted to the back-end computer for analysis. This also causes the limitation of daily applications. In this study, a wearable motor-imagery-based BCI system with the least number of EEG channels was developed. In this system, the wearable mechanical design and the wireless EEG acquisition module were designed for measuring real time EEG in daily life. The modified BCI algorithm was also developed, and here only three channels are used to distinguish different events of motor imagery in this study. Finally, the proposed BCI system was also validated by the hand-motor imagery experiment.
Development of a Wearable Motor-Imagery-Based Brain-Computer Interface
(2016-03-01)
Lin, Bor-Shing
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Pan, Jeng-Shyang
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Chu, Tso-Yao
;
Lin, Bor-Shyh
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影像與生醫光電研究所
A motor-imagery-based brain-computer interface (BCI) is a translator that converts the motor intention of the brain into a control command to control external machines without muscles. Numerous motor-imagery-based BCIs have been successfully proposed in previous studies. However, several electroencephalogram (EEG) channels are typically required for providing sufficient information to maintain a specific accuracy and bit rate, and the bulk volume of these EEG machines is also inconvenient. A wearable motor imagery-based BCI system was proposed and implemented in this study. A wearable mechanical design with novel active comb-shaped dry electrodes was developed to measure EEG signals without conductive gels at hair sites, which is easy and convenient for users wearing the EEG machine. In addition, a wireless EEG acquisition module was also designed to measure EEG signals, which provides a user with more freedom of motion. The proposed wearable motor-imagery-based BCI system was validated using an electrical specifications test and a hand motor imagery experiment. Experimental results showed that the proposed wearable motor-imagery-based BCI system provides favorable signal quality for measuring EEG signals and detecting motor imagery.