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Biometrics based on electroencephalogram (EEG) signals is an emerging research topic. Several recent results have shown its feasibility and potential for personal identification. However, they all use a single task (e.g., signals recorded during imagination of repetitive left hand movements or during resting with eyes open) for classifier design and subsequent identification. In contrast with this,...
Energy is very important in electroencephalogram (EEG) signal classification. In this paper, a criterion called extreme energy difference (EED) is devised, which is a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EED criterion, and therefore EED can be considered...
Classification of time-varying electrophysiological signals is an important problem in the development of brain-computer interfaces (BCIs). Designing adaptive classifiers is a potential way to address this task. In this paper, Bayesian classifiers with Gaussian mixture models (GMMs) are adopted as the decision rule to classify electroencephalogram (EEG) signals. The stochastic approximation method...
This paper introduces an ensemble approach for electroencephalogram (EEG) signal classification, which aims to overcome the instability of the Fisher discriminant feature extractor for brain-computer interface (BCI) applications. Through the random selection of electrodes from candidate electrodes, multiple individual classifiers are constructed. In a feature subspace determined by a couple of randomly...
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