Browsing by Author "Chou, Yu-Hsiang"

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A Geometrically Faithful Memetic Algorithm for Searching Sparse Representations of EEG Signals
(2012)
Qiu, Jun-Wei
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Zao, John K.
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Chou, Yu-Hsiang
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資訊工程學系
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Department of Computer Science
This paper presents a memetic algorithm, christened the natural memetic pursuit (NMP), that was designed to search for sparse signal representations. This algorithm combines global sampling based on a Grassmannian dictionary of atomic signals with local search using natural gradient decent in the signal parameter space. Performance of the algorithm was demonstrated by analyzing Jung and Makig's ERP data sets. It can obtain unbiased sparse representations of EEG signals in far less iteration than its predecessor, the stochastic matching pursuit (SMP).
An fMRI Study of Abrupt-Awake Episodes during Behavioral Microsleeps
(2011)
Chou, Yu-Hsiang
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Chuang, Chun-Chao
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Zao, John K.
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Ko, Li-Wei
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Lin, Chin-Teng
This paper reports the brain activation patterns of five subjects who were abruptly awakened from microsleeps in a simulated automotive driving experiment. By comparing the BOLD signals between behavioral microsleep (BM), abrupt awakening (AA) and post-abrupt awakening (post-AA) stages, we observed that visual area, frontal cortex, limbic lobe manifested more intense activation during the AA stage while frontal cortex, temporal cortex, primary motor area and insula were more activated during the post-AA stage. These results suggested that the subjects were likely in mental states differ from those associated with decision making processes as they went through and emerged from the abrupt awakening episodes.
Consistent Sparse Representations of EEG ERP and ICA Components Based on Wavelet and Chirplet Dictionaries
(2010)
Qiu, Jun-Wei
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Zao, John K.
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Wang, Peng-Hua
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Chou, Yu-Hsiang
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資訊工程學系
a randomized search algorithm for sparse representations of EEG event-related potentials (ERPs) and their statistically independent components is presented. This algorithm combines greedy matching pursuit (MP) technique with covariance matrix adaptation evolution strategy (CMA-ES) to select small number of signal atoms from over-complete wavelet and chirplet dictionaries that offer best approximations of quasi-sparse ERP signals. During the search process, adaptive pruning of signal parameters was used to eliminate redundant or degenerative atoms. As a result, the CMA-ES/MP algorithm is capable of producing accurate efficient and consistent sparse representations of ERP signals and their ICA components. This paper explains the working principles of the algorithm and presents the preliminary results of its use.
Continuous slip-ring SPECT acquisition with a five-sphere phantom and arbitrary time-frame shifting reconstruction to simulate a dynamic drug challenge study
(2010-05-01)
Kao, Pan-Fu
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Shiu, Ruei-Lin
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Lee, Jia-Hung
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Chou, Yu-Hsiang
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資訊工程學系