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Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to...
Brain Computer Interfaces (BCI) have been emerged as an alternative to support automatic systems able to interpret brain functions, commonly, by analyzing electroencephalography (EEG) recordings. In this work, a time-series discrimination methodology, called Motor Imagery Discrimination by Relevance Analysis (MIDRA), is presented to support the development of BCI from EEG data. Particularly, a Motor...
Category 4. A machine learning based methodology is proposed to recognize a predefined set of hand gestures using depth images. For such purpose, a RGBD sensor (Microsoft kinect) is employed to track the hand position. Thus, a preprocessing stage is presented to subtract the region of interest from depth images. Moreover, a learning algorithm based on kernel methods is used to discover the relationships...
In this paper we analyze high-dimensional data by means of the manifold learning algorithm Locally Linear Embedding. We employ this method to visually analyze both artificial and real-world datasets lying on nonlinear structures, comparing its transformations against the traditional feature extraction technique Principal Components Analysis. Moreover, we propose a data synthesis scheme based on manifold...
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