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Deep learning solutions based on deep neural networks (DNN) and deep stack networks (DSN) were investigated for classifying target images in a non-time-locked rapid serial visual presentation (RSVP) image target identification task using EEG. Several feature extraction methods associated with this task were implemented and tested for deep learning, where a(More)
Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which(More)
Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space(More)
OBJECTIVE In this paper we present and test a new method for the identification and removal of non-stationary utility line noise from biomedical signals. METHODS The method, Band Limited Atomic Sampling with Spectral Tuning (BLASST), is an iterative approach that is designed to (1) fit non-stationarities in line noise by searching for best-fit Gabor atoms(More)
BACKGROUND This experimental study evaluated the interobserver reliability and accuracy of pre-operative digital templating for humeral head size, stem size and neck angle for total shoulder arthroplasty. METHODS Twenty-five patients underwent a total shoulder arthroplasty with a single prosthesis. Four independent, blinded surgeons (two experienced(More)
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