AIMC Topic: Electrocorticography

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Brain-optimized extraction of complex sound features that drive continuous auditory perception.

PLoS computational biology Jul 2, 2020
Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match ...
Adolescent Time Factors Models, Neurological Brain Mapping Young Adult Adult Male Speech Phonetics Electrocorticography Auditory Perception Motion Pictures Signal Processing, Computer-Assisted Sound Auditory Cortex Speech Perception Humans Neural Networks, Computer Female
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Machine translation of cortical activity to text with an encoder-decoder framework.

Nature neuroscience Mar 30, 2020
A decade after speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech. Here we show how to decode the electrocorticogram with high accuracy and at natural-speech rates. Taking a cue from recent a...
Middle Aged Speech Neural Networks, Computer Brain-Computer Interfaces Humans Electrocorticography Speech Perception Brain Female Adult
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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 Pattern Recognition, Automated Brain-Computer Interfaces Electrocorticography Macaca mulatta Animals Male Prefrontal Cortex Action Potentials Spatial Memory Saccades
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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...
Biomedical Engineering Electrodes, Implanted Electrophysiological Phenomena Evoked Potentials Verbal Behavior Electrocorticography Cognition Unsupervised Machine Learning Brain Mapping Electroencephalography Epilepsy Algorithms Memory, Short-Term Brain Datasets as Topic Retrospective Studies Task Performance and Analysis Sensitivity and Specificity Humans
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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.
Temporal Lobe Foramen Ovale ROC Curve Area Under Curve Artifacts Adult Datasets as Topic Humans Sensitivity and Specificity Reference Standards Algorithms Male Epilepsy, Temporal Lobe Female Deep Learning Electrocorticography Electrodes, Implanted
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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...
Cerebral Cortex Electrocorticography Deep Learning Epilepsy Seizures Signal Processing, Computer-Assisted Humans Adult Sensitivity and Specificity
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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 ...
Humans Deep Learning Speech Computational Biology Signal Processing, Computer-Assisted Electrocorticography Sensorimotor Cortex
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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 ...
Epilepsy Seizures Electrocorticography Humans Neural Networks, Computer Support Vector Machine Electroencephalography
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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 Brain Female Middle Aged Brain Mapping Nerve Net Aged Electrocorticography Young Adult Adult Male
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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...
Electroencephalography Epilepsy Seizures Electrocorticography Female Humans Male Neural Networks, Computer Deep Learning Precision Medicine Electrocardiography
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