Browsing by Author "Yang, Fu-Shu"

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EEG-based Spatial Navigation Estimation in a Virtual Reality Driving Environment
(2009)
Lin, Chin-Teng
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Yang, Fu-Shu
;
Chiou, Te-Chung
;
Ko, Li-Wei
;
Duann, Jeng-Ren
The aim of this study is to investigate the difference of EEC dynamics on navigation performance. A tunnel task was designed to classify subjects into allocentric or egocentric spatial representation users. Despite of the differences of mental spatial representation, behavioral performance in general were compatible between the two strategies subjects in the tunnel task. Task-related EEC dynamics in power changes were analyzed using independent component analysis (ICA), time-frequency and non-parametric statistic test. ERSP image results revealed navigation performance-predictive EEG activities which is is expressed in the parietal component by source reconstruction. For egocentric subjects, comparing to trails with well-estimation of homing angle, the power attenuation at the frequencies from 8 to 30 Hz (around alpha and beta band) was stronger when subjects overestimated homing directions, but the attenuated power was decreased when subjects were underestimated the homing angles. However, we did not found performance related brain activities for allocentric subjects, which may due to the functional dissociation between the use of allo- and egocentric reference frames.
Real-Time Embedded EEG-Based Brain-Computer Interface
(2009)
Ko, Li-Wei
;
Tsai, I-Ling
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Yang, Fu-Shu
;
Chung, Jen-Feng
;
Lu, Shao-Wei
Online artifact rejection, feature extraction, and pattern recognition are essential to advance the Brain Computer Interface (BCI) technology so as to be practical for real-world applications. The goals of BCI system should be a small size, rugged, lightweight, and have low power consumption to meet the requirements of wearability, portability, and durability. This study proposes and implements a moving-windowed Independent Component Analysis (ICA) on a battery-powered, miniature, embedded BCI. This study also tests the embedded BCI on simulated and real EEG signals. Experimental results indicated that the efficacy of the online ICA decornposition is comparable with that of the offline version of the same algorithm, suggesting the feasibility of ICA for online analysis of EEG in a BCI. To demonstrate the feasibility of the wearable embedded BCI, this study also implements an online spectral analysis to the resultant component activations to continuously estimate subject's task performance in near real time.
Wearable and Wireless Brain-Computer Interface and Its Applications
(2009)
Lin, Chin-Teng
;
Ko, Li-Wei
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Chang, Che-Jui
;
Wang, Yu-Te
;
Chung, Chia-Hsin
This study extends our previous work on mobile & wireless EEG acquisition to a truly wearable and wireless human-machine interface, NCTU Brain-Computer-Interface-headband,(BCI-headband), featuring: (1) dry Micro-Electro-Mechanical System (MEMS) EEG electrodes with 400 ganged contacts for acquiring signals from non-hairy sites without use Of gel or skin preparation; (2) a miniature data acquisition circuitry, (3) wireless telemetry and (4) online signal processing on a commercially available cell phone or a lightweight, wearable digital signal processing module. The applicability of the NCTU BCI headband to EEG monitoring in real-world environments was demonstrated in a sample study: cognitive-state monitoring and management of participants performing normal tasks.