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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chen, Shi-AnSubject: search.filters.subject.Brain-Computer Interface (BCI)

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    A Novel Classification Method for Motor Imagery Based on Brain-Computer Interface
    (2014)
    Chen, Chih-Yu
    ;
    Wu, Chun-Wei
    ;
    Lin, Chin-Teng
    ;
    Chen, Shi-An
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    電機學院
    Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.
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    Common Spatial Pattern and Linear Discriminant Analysis for Motor Imagery Classification
    (2013)
    Wu, Shang-Lin
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    Wu, Chun-Wei
    ;
    Pal, Nikhil R.
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    Chen, Chih-Yu
    ;
    Chen, Shi-An
    A Brain-Computer Interface (BCI) system provides a convenient way of communication for healthy subjects and subjects who suffer from severe diseases such as amyotrophic lateral sclerosis (ALS). Motor imagery (MI) is one of the popular ways of designing BCI systems. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. We have also experimented with MI data generated in our lab. The proposed system is found to produce good results. In particular, using our EEG data for MI movements, we have obtained an average classification accuracy of 80% for two subjects using only 9 channels, without any feature selection. This proposed MI-based BCI system may be used in real life applications.
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    The Influence of Acute Stress on Brain Dynamics
    (2013)
    Lin, Chin-Teng
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    Wang, Yu-Kai
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    Fan, Jhe-Wei
    ;
    Chen, Shi-An
    ;
    資訊工程學系
    Living under high stress may be unhealthy. This study explores electroencephalography (EEG) correlated with stressful circumstances by using the task-switching paradigm with feedback information. According to the behavioral and physiological evidence, acute stress created by this paradigm affected the performance of participants. Under stress, the participants responded quickly and inaccurately. The EEG results correlated with acute stress were found in the frontal midline cortex, especially on the theta and alpha bands. These specific factors are considered importance features for detecting the influence of stress by applying various machine-learning methods and neuro-fuzzy systems. This comprehensive study can provide knowledge for studying stress and designing Brain-Computer Interface (BCI) systems in the future.