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The advent of mobile DNA sequencers has made it possible to generate DNA sequencing data outside of laboratories and genome centers. Here, we report our experience of using the MinION, a mobile sequencer, in a 13-week academic course for undergraduate and graduate students. The course consisted of theoretical sessions that presented fundamental topics in(More)
OBJECTIVE To summarize the characteristics of clinical features, laboratory tests, treatments and outcomes of childhood systemic lupus erythematosus (SLE). METHODS A retrospective study was conducted for 130 cases of first-confirmed childhood SLE hospitalized from 2001 to 2008. Their clinical data of initial manifestations, system involvements, laboratory(More)
This paper proposes an improved method for generating different phases of visual stimulus while liquid crystal display (LCD)/cathode ray tube (CRT) is employed as the visual stimulator. Since using the traditional method can only generate the limited frequencies and phases of visual stimulus, increasing the number of different flickering targets becomes(More)
Classification of electroencephalogram (EEG) is a crucial issue for EEG-based brain computer interface (BCI) system. In this paper, the performances of the Gaussian process classifier (GPC) for three different categories of EEG signals, i.e. steady state visually evoked potential (SSVEP), motor imagery and finger movement EEG data, are investigated. The(More)
Gaussian mixture model (GMM) has been considered to model the EEG data for the classification task in brain-computer interface (BCI) system. In the practical BCI application, however, the performance of the classical GMM optimized by standard expectation-maximization (EM) algorithm may be degraded due to the noise and outliers, which often exist in(More)
While both cost-sensitive learning and online learning have been studied separately, these two issues have seldom been addressed simultaneously. Yet, there are many applications where both aspects are important. This paper investigates a class of algorithmic approaches suitable for online cost-sensitive learning, designed for such problems. The basic idea(More)
The performances of different off-line methods for two different Electroencephalograph (EEG) signal classification tasks - motor imagery and finger movement, are investigated in this paper. The classifiers based on linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kernel fisher discriminant (KFD), support vector machine (SVM),(More)