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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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Author: search.filters.author.Chikara, Rupesh KumarSubject: search.filters.subject.EEG

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    Phase modulation-Based Response-Inhibition Outcome Prediction in Translational Scenario of Stop-Signal task
    (2016-01-01)
    Chikara, Rupesh Kumar
    ;
    Ko, Li-Wei
    ;
    生物科技學系
    ;
    腦科學研究中心
    ;
    Department of Biological Science and Technology
    In this paper, a method is proposed to predict the resting-state outcomes of participants based on their electroencephalogram (EEG) signals recorded before the successful /unsuccessful response inhibition. The motivation of this study is to enhance the shooter performance for shooting the target, when their EEG patterns show that they are ready. This method can be used in brain-computer interface (BCI) system. In this study, multi-channel EEG from twenty participants are collected by the electrodes placed at different scalp locations in resting-state time. The EEG trials are used to predict two possible outcomes: successful or unsuccessful stop. Four classifiers (QDC, KNNC, PARZENDC, LDC) are used in this study to evaluation the accuracy of our system. Based on the collected time-domain EEG signals, the phase locking value (PLV) from 5-pair electrodes are calculated and then used as the feature input for the classifiers. Our experimental results show that the proposed method prediction accuracy (leave-one-out) was obtained 95% by QDC classifier.
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    Item type:Item,
    Classification of EEG-P300 Signals Using Phase Locking Value and Pattern Recognition Classifiers
    (2015)
    Chikara, Rupesh Kumar
    ;
    Ko, Li-Wei
    ;
    生物科技學系
    ;
    生物資訊及系統生物研究所
    ;
    腦科學研究中心
    In this paper, we present a classification method based on electroencephalogram (EEG) signal during left hand and right hand response inhibition (stop success vs stop fail) from different participants. The system uses phase locking value (PLV) for the features extraction and pattern recognition algorithm for classification. There are four classifiers: QDC, KNNC, PARZENDC and LDC used in this paper to estimate the accuracy of our system. Based on the collected time-domain EEG signals, the phase locking value (PLV) from C3-CZ and C4- CZ electrodes are calculated and then used as the feature and input for the classifiers algorithm. The classification system demonstrate an accuracy of 92 % in LDC. The results of this study suggest the method could be utilized effectively for response inhibition identification.