AIMC Topic: Electrocorticography

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A neural network for online spike classification that improves decoding accuracy.

Journal of neurophysiology Feb 26, 2020
Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on dec...
Neural Networks, Computer Animals Male Prefrontal Cortex Electrocorticography Macaca mulatta Action Potentials Spatial Memory Pattern Recognition, Automated Saccades Brain-Computer Interfaces
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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.

Scientific reports Nov 22, 2019
Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to class...
Humans Algorithms Epilepsy Memory, Short-Term Datasets as Topic Cognition Verbal Behavior Biomedical Engineering Unsupervised Machine Learning Electrocorticography Brain Task Performance and Analysis Electrodes, Implanted Brain Mapping Retrospective Studies Evoked Potentials Electrophysiological Phenomena Electroencephalography Sensitivity and Specificity
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Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Nov 11, 2019
OBJECTIVE: Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.
Area Under Curve Artifacts Datasets as Topic Reference Standards Male Deep Learning ROC Curve Temporal Lobe Foramen Ovale Epilepsy, Temporal Lobe Electrocorticography Electrodes, Implanted Humans Adult Sensitivity and Specificity Algorithms Female
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Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network.

International journal of neural systems Sep 30, 2019
Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work, a novel approach, combining Stockwell transform (S-transform) with deep Convolutional Neural Networ...
Electrocorticography Adult Humans Sensitivity and Specificity Epilepsy Signal Processing, Computer-Assisted Deep Learning Seizures Cerebral Cortex
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Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.

PLoS computational biology Sep 16, 2019
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear ...
Electrocorticography Sensorimotor Cortex Speech Computational Biology Signal Processing, Computer-Assisted Humans Deep Learning
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Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies.

Seizure Aug 19, 2019
Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a ...
Electrocorticography Support Vector Machine Electroencephalography Epilepsy Seizures Humans Neural Networks, Computer
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Dynamic network modeling and dimensionality reduction for human ECoG activity.

Journal of neural engineering Aug 14, 2019
OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far...
Humans Neural Networks, Computer Aged Young Adult Brain Female Adult Brain Mapping Male Electrocorticography Nerve Net Middle Aged
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Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.

EBioMedicine Jul 9, 2019
BACKGROUND: The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent advances have paved the way for increasingly accurate seizure preictal state detection algorithms, pri...
Electrocorticography Humans Neural Networks, Computer Precision Medicine Electrocardiography Epilepsy Female Electroencephalography Seizures Male Deep Learning
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Human visual cortical gamma reflects natural image structure.

NeuroImage Jun 24, 2019
Many studies have reported visual cortical gamma-band activity related to stimulus processing and cognition. Most respective studies used artificial stimuli, and the few studies that used natural stimuli disagree. Electrocorticographic (ECoG) recordi...
Humans Electrocorticography Functional Neuroimaging Pattern Recognition, Visual Support Vector Machine Male Visual Cortex Gamma Rhythm Middle Aged
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Deep-learning for seizure forecasting in canines with epilepsy.

Journal of neural engineering Apr 8, 2019
OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy...
Electrocorticography Epilepsy Forecasting Animals Seizures Electrodes, Implanted Deep Learning Dogs
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