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This paper studies the supervised classification of electroencephalogram (EEG) brain signals to identify persons and their activities. The brain signals are obtained from a commercially available and modestly priced wearable headband. Such wearable devices generate a large amount of data and due to their attractive pricing structure are becoming increasingly commonplace. As a result, the data generated...
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the...
Feature selection is an important step for data mining and machine learning to deal with the curse of dimensionality. In this paper, we propose a novel feature selection framework based on supervised data clustering. Instead of assuming there only exists low-order dependencies between features and the target variable, the proposed method directly estimates the high-dimensional mutual information between...
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