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Teng-Yi Huang

Researcher at National Chiao Tung University

Publications -  11
Citations -  733

Teng-Yi Huang is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Alertness & Driving simulator. The author has an hindex of 10, co-authored 11 publications receiving 658 citations. Previous affiliations of Teng-Yi Huang include University System of Taiwan.

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

TL;DR: 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.
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Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks

TL;DR: An adaptive alertness estimation methodology based on electroencephalogram, power spectrum analysis, independent component analysis (ICA), and fuzzy neural network (FNNs) models is proposed in this paper for continuously monitoring driver's drowsiness level with concurrent changes in the alertness level.
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Estimating driving performance based on EEG spectrum analysis

TL;DR: An EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsness level in a virtual-reality-based driving simulator is proposed.
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An EEG-Based Fatigue Detection and Mitigation System.

TL;DR: Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.
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Gender differences in wayfinding in virtual environments with global or local landmarks

TL;DR: In this article, the authors assess gender differences in wayfinding in environments with global or local landmarks by analyzing both overall and fine-grained measures of performance, and conclude that males and females are differentially superior when using global and local landmark information, respectively.