Brant: Foundation model for intracranial neural signal
We propose a foundation model named Brant for modeling intracranial recordings, which
learns powerful representations of intracranial neural signals by pre-training, providing a …
learns powerful representations of intracranial neural signals by pre-training, providing a …
EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection
Epilepsy is a common non-communicable, group of neurological disorders affecting more
than 50 million individuals worldwide. Researchers are working to automatically detect …
than 50 million individuals worldwide. Researchers are working to automatically detect …
Ppi: Pretraining brain signal model for patient-independent seizure detection
Automated seizure detection is of great importance to epilepsy diagnosis and treatment. An
emerging method used in seizure detection, stereoelectroencephalography (SEEG), can …
emerging method used in seizure detection, stereoelectroencephalography (SEEG), can …
[HTML][HTML] SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy
Objective Precise preoperative evaluation of drug-resistant epilepsy (DRE) requires
accurate analysis of invasive stereoelectroencephalography (SEEG). With the tremendous …
accurate analysis of invasive stereoelectroencephalography (SEEG). With the tremendous …
Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review
Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using
routine diagnostic tests like Electroencephalography (EEG). However, manual introspection …
routine diagnostic tests like Electroencephalography (EEG). However, manual introspection …
Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification
Manual visual review, annotation and categorization of electroencephalography (EEG) is a
time-consuming task that is often associated with human bias and requires trained …
time-consuming task that is often associated with human bias and requires trained …
Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation
Background While the effects of prolonged sleep deprivation (≥ 24 h) on seizure
occurrence has been thoroughly explored, little is known about the effects of day-to-day …
occurrence has been thoroughly explored, little is known about the effects of day-to-day …
Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis
Objective. The current practices of designing neural networks rely heavily on subjective
judgment and heuristic steps, often dictated by the level of expertise possessed by …
judgment and heuristic steps, often dictated by the level of expertise possessed by …
High‐Resolution Recording of Neural Activity in Epilepsy Using Flexible Neural Probes
Epilepsy, a prevalent neurological disorder, necessitates precise and reliable
electrophysiological recording for accurate study and diagnosis. Although traditional stereo …
electrophysiological recording for accurate study and diagnosis. Although traditional stereo …
Privacy-Preserving Domain Adaptation for Intracranial EEG Classification via Information Maximization and Gaussian Mixture Model
Automated deep learning methods for classifying intracranial electroencephalography
(iEEG) recordings into three categories (artifacts, pathological activities, and physiological …
(iEEG) recordings into three categories (artifacts, pathological activities, and physiological …