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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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Access type: Metadata onlySubject: search.filters.subject.Brain-computer interface

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Now showing 1 - 6 of 6
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    A Scoping Study on the Development of an Interactive Upper-Limb Rehabilitation System Framework for Patients with Stroke
    (2018-01-01)
    Tseng, Kevin C.
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    Wong, Alice M. K.
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    Wu, Chung-Yu
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    Chang, Tian-Sheuan
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    Pei, Yu-Cheng
    This study aims to propose the framework of the interactive upper-limb rehabilitation system with brain-computer interfaces. The system mainly includes an interactive rehabilitation training platform, a rehabilitation database system, and an EEG and EMG acquisition system. The interactive rehabilitation training system platform includes a virtual rehabilitation game system and an interactive upper-limb rehabilitation device by which a user can perform proactive and reactive rehabilitation.
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    Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot
    (2020-01-01)
    Phang, Chun-Ren
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    Ko, Li-Wei
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    交大名義發表
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    生物資訊及系統生物研究所
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    National Chiao Tung University
    Conventional passive lower limb rehabilitation is suboptimal since the brain is not actively involved in the training. An autonomous motor imagery brain-computer interface (MI-BCI) could potentially improve rehabilitation outcomes. However, motor cortex regions associated with the individual feet are anatomically close to each other. This presents a difficulty in distinguishing the left and right foot MI during rehabilitation therapy. To overcome this difficulty, we extracted functional connectivity to measure the global cortical network via electroencephalography (EEG) signals. Fourteen spatial connections (P3-Fp1, P3-F3, P3-F7, P3-C3, T5-F7, T5-C3, T5-T3, Fp2-T5, Fp2-P3, T6-Fp2, T6-T4, Cz-Fp1, Cz-F7 and Fp2-F7) found across twelve subjects significantly differed between the left and right foot MI, evidencing nonlocalized brain activity during MI. Foot MI were distinguished using machine learning algorithms in terms of the time- and frequency-domain connectivities extracted from Pearson & x2019;s correlation, multivariate autoregression (MVAR), bandpass correlation, and partial directed coherence (PDC) models. The results showed that connectivity extracted by pairwise Pearson & x2019;s correlation could be distinguished with 86.26 & x00B1; 9.95 & x0025;, while in the frequency-domain, the gamma band presented the best classification accuracy of 73.55 & x00B1; 17.11 & x0025;. We attempted to simulate asynchronous real-time classification paradigms in order to evaluate the classification performance of connectivity features compared to common spatial pattern (CSP) and band power (BP). The results indicate correlation-connectivity has the best outcome, attaining an accuracy of 80.75 & x00B1; 9.51 & x0025; in asynchronous classification.
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    Spectral Meta-Learner for Regression (SMLR) Model Aggregation: Towards Calibrationless Brain-Computer Interface (BCI)
    (2016-01-01)
    Wu, Dongrui
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    Lawhern, Vernon J.
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    Gordon, Stephen
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    Lance, Brent J.
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    Lin, Chin-Teng
    To facilitate the transition of brain-computer interface (BCI) systems from laboratory settings to real-world application, it is very important to minimize or even completely eliminate the subject-specific calibration requirement. There has been active research on calibrationless BCI systems for classification applications, e.g., P300 speller. To our knowledge, there is no literature on calibrationless BCI systems for regression applications, e.g., estimating the continuous drowsiness level of a driver from EEG signals. This paper proposes a novel spectral meta-learner for regression (SMLR) approach, which optimally combines base regression models built from labeled data from auxiliary subjects to label offline EEG data from a new subject. Experiments on driver drowsiness estimation from EEG signals demonstrate that SMLR significantly outperforms three state-of-the-art regression model fusion approaches. Although we introduce SMLR as a regression model fusion in the BCI domain, we believe its applicability is far beyond that.
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    Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
    (2016-01-01)
    Wu, Dongrui
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    Lawhern, Vernon J.
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    Gordon, Stephen
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    Lance, Brent J.
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    Lin, Chin-Teng
    Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting, which assigns a sample to the class that most base classifiers vote for. However, improved performance can be obtained by assigning weights to the base classifiers according to their accuracy. This paper proposes an agreement rate initialized maximum likelihood estimator (ARIMLE) to optimally fuse the base classifiers. ARIMLE first uses a simplified agreement rate method to estimate the classification accuracy of each base classifier from the unlabeled samples, then employs the accuracies to initialize a maximum likelihood estimator (MLE), and finally uses the expectation-maximization algorithm to refine the MLE. Extensive experiments on visually evoked potential classification in a brain-computer interface application show that ARIMLE outperforms majority voting, and also achieves better or comparable performance with several other state-of-the-art classifier combination approaches.
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    Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
    (2017-12-01)
    Wu, Dongrui
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    Lawhern, Vernon J.
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    Gordon, Stephen
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    Lance, Brent J.
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    Lin, Chin-Teng
    One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation fromEEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.
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    A Hierarchal Classifier for Identifying Independent Components
    (2012)
    Lin, Chin-Teng
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    Wang, Yu-Kai
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    Chen, Shi-An
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    電控工程研究所
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    Institute of Electrical and Control Engineering
    Brain-computer interface (BCI) has shown explosive growth for multiple applications in the recently years. Removing artifacts and selecting useful brain sources are essential in BCI research. Independent Component Analysis (ICA) has been proven as an effective technique to remove artifacts and many brain related researches are based on ICA. However, the useful independent components with brain sources are usually selected manually according to the scalp-plots. This is great inconvenience and a barrier for real-time BCI applications of EEG. In this investigation, a two-layer automatic identification model is proposed to select useful brain sources. It is based on neural network including support vector machine with radial basis function (SVMRBF) and self-organizing map (SOM). In the first layer, SVM discriminates useful independent components from the artifact effectively. In the second layer, these selected useful components are automatically classified to different spatial brain sources according to SOM. This study suggests this model to one general application for EEG study. It can reduce the effect of subjective judgment and improve the performance of EEG analysis.