AIMC Topic: Electrocorticography

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Random ensemble learning for EEG classification.

Artificial intelligence in medicine Jan 3, 2018
Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rap...
Brain Mapping Predictive Value of Tests Electroencephalography Seizures Brain Reproducibility of Results Time Factors Support Vector Machine Wavelet Analysis Brain Waves Cloud Computing Humans Electrocorticography Neural Networks, Computer Signal Processing, Computer-Assisted Automation False Negative Reactions False Positive Reactions
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Decoding of finger trajectory from ECoG using deep learning.

Journal of neural engineering Nov 28, 2017
OBJECTIVE: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained...
Brain-Computer Interfaces Fingers Movement Photic Stimulation Electrocorticography Sensorimotor Cortex Humans Deep Learning
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Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 22, 2017
Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features, which utilized specific characteristics of neural responses. A...
Reproducibility of Results Young Adult Adult Male Neural Networks, Computer Machine Learning Algorithms Female Electrocorticography Electrodes, Implanted Telemetry Adolescent Seizures Nonlinear Dynamics Epilepsy, Temporal Lobe Humans
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A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

IEEE transactions on bio-medical engineering Sep 13, 2017
OBJECTIVE: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection.
Animals Dogs Support Vector Machine Electrocorticography Cluster Analysis Epilepsy Signal Processing, Computer-Assisted
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Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

Seizure Jul 25, 2017
PURPOSE: Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ).
Adult Male Adolescent Child, Preschool Algorithms Intraoperative Neurophysiological Monitoring Humans Retrospective Studies Seizures Machine Learning Female Electrocorticography Young Adult
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Unsupervised Learning of Spike Patterns for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic ( $\mu $ ECoG) Data.

IEEE transactions on nanobioscience Jun 9, 2017
For the past few years, we have developed flexible, active, and multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the ele...
Cerebral Cortex Electrocorticography Humans Reproducibility of Results Sensitivity and Specificity Diagnosis, Computer-Assisted Action Potentials Unsupervised Machine Learning Seizures Pattern Recognition, Automated
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Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy.

eNeuro Mar 8, 2017
Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How do...
Humans Female Male Time Factors Cluster Analysis Drug Resistant Epilepsy Models, Neurological Electrocorticography Unsupervised Machine Learning Electrodes, Implanted Brain Mapping Neocortex Seizures Datasets as Topic
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SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

IEEE transactions on bio-medical engineering Jun 29, 2016
OBJECTIVE: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics o...
Pattern Recognition, Automated Electrocorticography Diagnosis, Computer-Assisted Support Vector Machine Epilepsy Dogs Algorithms Humans Reproducibility of Results Sensitivity and Specificity Animals
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Seizure Forecasting and the Preictal State in Canine Epilepsy.

International journal of neural systems Jun 14, 2016
The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but ma...
Time Factors ROC Curve Disease Models, Animal Area Under Curve Machine Learning Epilepsy Electrocorticography Prognosis Animals Seizures Wireless Technology Signal Processing, Computer-Assisted Electrodes, Implanted Dogs
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Decoding intracranial EEG data with multiple kernel learning method.

Journal of neuroscience methods Dec 12, 2015
BACKGROUND: Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their app...
Pattern Recognition, Visual Pattern Recognition, Automated Signal Processing, Computer-Assisted Humans Memory, Episodic Feasibility Studies Machine Learning Rest Drug Resistant Epilepsy Brain Datasets as Topic Neuropsychological Tests Electrocorticography Self Concept Semantics Judgment Mathematical Concepts
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