AIMC Topic: Electrocorticography

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Encoding of Articulatory Kinematic Trajectories in Human Speech Sensorimotor Cortex.

Neuron
When speaking, we dynamically coordinate movements of our jaw, tongue, lips, and larynx. To investigate the neural mechanisms underlying articulation, we used direct cortical recordings from human sensorimotor cortex while participants spoke natural ...

Sub-millimeter ECoG pitch in human enables higher fidelity cognitive neural state estimation.

NeuroImage
Electrocorticography (ECoG), electrophysiological recording from the pial surface of the brain, is a critical measurement technique for clinical neurophysiology, basic neurophysiology studies, and demonstrates great promise for the development of neu...

Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy.

Journal of neurophysiology
The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Ele...

Random ensemble learning for EEG classification.

Artificial intelligence in medicine
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...

Decoding of finger trajectory from ECoG using deep learning.

Journal of neural engineering
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...

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
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...

A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection.

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

Seizure
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).

Unsupervised Learning of Spike Patterns for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic ( $\mu $ ECoG) Data.

IEEE transactions on nanobioscience
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...

Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy.

eNeuro
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...