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Adaptive epileptic seizure prediction system
Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is thenExpand
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Long-term prospective on-line real-time seizure prediction
OBJECTIVE Epilepsy, one of the most common neurological disorders, constitutes a unique opportunity to study the dynamics of spatiotemporal state transitions in real, complex, nonlinear dynamicalExpand
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KINALYZER, a computer program for reconstructing sibling groups.
A software suite KINALYZER reconstructs full-sibling groups without parental information using data from codominant marker loci such as microsatellites. KINALYZER utilizes a new algorithm for siblingExpand
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A new linearization technique for multi-quadratic 0-1 programming problems
We consider the reduction of multi-quadratic 0-1 programming problems to linear mixed 0-1 programming problems. In this reduction, the number of additional continuous variables is O(kn) (n is theExpand
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Chemometrics and Intelligent Laboratory Systems
abstract Article history:Received 19 December 2008Received in revised form 27 February 2009Accepted 4 March 2009Available online 13 March 2009Keywords:Variable selectionPLS regressionk-nearestExpand
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Using Wireless EEG Signals to Assess Memory Workload in the $n$-Back Task
Assessment of mental workload using physiological measures, especially electroencephalography (EEG) signals, is an active area. Recently, a number of wireless acquisition systems to measure EEG andExpand
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Reconstructing sibling relationships in wild populations
Reconstruction of sibling relationships from genetic data is an important component of many biological applications. In particular, the growing application of molecular markers (microsatellites) toExpand
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Performance of a seizure warning algorithm based on the dynamics of intracranial EEG
During the past decade, several studies have demonstrated experimental evidence that temporal lobe seizures are preceded by changes in dynamical properties (both spatial and temporal) ofExpand
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Online Seizure Prediction Using an Adaptive Learning Approach
Epilepsy is one of the most common neurological disorders, characterized by recurrent seizures. Being able to predict impending seizures could greatly improve the lives of patients with epilepsy. InExpand
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On the Time Series $K$-Nearest Neighbor Classification of Abnormal Brain Activity
Epilepsy is one of the most common brain disorders, but the dynamical transitions to neurological dysfunctions of epilepsy are not well understood in current neuroscience research. UncontrolledExpand
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