AIMC Topic: Electrocorticography
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XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.
NeuroImage
Aug 22, 2024
Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging...
Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.
Journal of neuroscience methods
Aug 14, 2024
BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing m...
Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.
Nature communications
Apr 16, 2024
Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-si...
Timing matters for accurate identification of the epileptogenic zone.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Feb 18, 2024
OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intra...
Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.
Journal of neural engineering
Sep 20, 2023
Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from...
Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.
NeuroImage
Sep 15, 2023
Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and th...
Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis.
Journal of neural engineering
Jun 16, 2023
The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process,...
Accessing the brain with soft deployable electrocorticography arrays.
Science robotics
May 10, 2023
Soft robotics facilitates the deployment of large radial electrode arrays on the brain cortex through small craniotomies.
Deployment of an electrocorticography system with a soft robotic actuator.
Science robotics
May 10, 2023
Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery and increasingly explored in brain-machine interface applications. Current devic...
Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy.
IEEE journal of biomedical and health informatics
Nov 10, 2022
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortic...
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