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This paper considers the problem of classification of electroencephalography (EEG) recordings without the precise time locking between stimulus presentation times and the recorded EEG waveforms. Traditionally, time locking, or perfect timing, information between stimulus and EEG recordings have been crucial in locating the region of possible neural response. In reality, the stimulus' time information...
Uncovering transcription factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the factor for modeling large networks, a novel, efficient basis-expansion factor (BE-FaM) model has been proposed, where the loading...
In this paper, the problem of automatic characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data is considered. A novel method that aims at identifying event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied...
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