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Power consumption is a major concern in today's processor design. As technology shrinks, leakage power dominates the overall power consumption of the processor although it is expected that dynamic power gains relevance in future semiconductor technology. This is particularly relevant for the cache hierarchy, which contains an important percentage of the(More)
The Neural Engineering Data Consortium (NEDC) is releasing its first major big data corpus - the Temple University Hospital EEG Corpus. This corpus consists of over 25,000 EEG studies, and includes a neurologist's interpretation of the test, a brief patient medical history and demographic information about the patient such as gender and age. For the first(More)
Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to(More)
The interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiners. Though interrater agreement on critical events such as seizures is high, it is much lower on subtler events (e.g., when there are benign variants). The process used by an expert to interpret an EEG is quite subjective and hard(More)
Feature extraction for automatic interpretation of EEGs has been extensively studied. A number of commercial approaches use exotic feature sets such as wavelets or nonlinear statistical measures such as fractal dimension. These choices of features were the results of evaluations and optimizations conducted on small research databases often collected under(More)
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