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This paper investigates how to apply active learning for the classification of motor imagery electroencephalography (EEG) signals to boost the performance for small training size. A new criterion is proposed to select the most representative and informative queries. The candidates are firstly chosen from the samples close to the center of the cluster that has the highest impurity of classes. A predefined...
This paper proposes a novel active learning method for the classification of motor imagery electroencephalogram (EEG) signals. Specifically, we propose an iterative clustering and support vector-based criterion to select samples of high-confidence to construct a robust training set. The common spatial pattern (CSP)-based features are iteratively clustered till the number of support vectors in the...
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