AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Electrocorticography

Showing 51 to 60 of 67 articles

Clear Filters

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...

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 ...

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...

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...

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.

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate.

Computational intelligence and neuroscience
Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studie...

Modeling Neural Adaptation in Auditory Cortex.

Frontiers in neural circuits
Neural responses recorded from auditory cortex exhibit adaptation, a stimulus-specific decrease that occurs when the same sound is presented repeatedly. Stimulus-specific adaptation is thought to facilitate perception in noisy environments. Although ...

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...

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...

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).