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Found 7 papers, 3 papers with code
Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology
The current clinical EEG review process relies heavily on expert visual review, which is unscalable and error-prone.
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
The recent availability of large datasets in bio-medicine has inspired the development of representation learning methods for multiple healthcare applications.
SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports
Machine learning (ML)-based analysis of electroencephalograms (EEGs) is playing an important role in advancing neurological care.
EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network
This paper presents a novel graph convolutional neural network (GCNN)-based approach for improving the diagnosis of neurological diseases using scalp-electroencephalograms (EEGs).
Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy
An ability to map seizure-generating brain tissue, i. e., the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy.
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials
However, those approaches have failed to account for the variability among participants that is becoming increasingly evident as a result of recent clinical-trial-based studies.
EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms
This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG).
