[HTML][HTML] Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our
understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy …
understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy …
[HTML][HTML] Analysis of electrode performance on amplitude integrated electroencephalography in neonates: evaluation of a new electrode aCUP-E vs. liquid gel …
A Fabregat-Sanjuan, Á Rodríguez-Ballabriga… - Frontiers in …, 2024 - frontiersin.org
Background Neonatologists and clinical neurophysiologists face challenges with the current
electrodes used for long-duration amplitude-integrated electroencephalography (aEEG) in …
electrodes used for long-duration amplitude-integrated electroencephalography (aEEG) in …
Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.
Y Liu, Y Jiang, J Liu, J Li, M Liu, W Nie… - International Journal of …, 2024 - europepmc.org
Automatic seizure detection has significant value in epilepsy diagnosis and treatment.
Although a variety of deep learning models have been proposed to automatically learn …
Although a variety of deep learning models have been proposed to automatically learn …
[HTML][HTML] Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detection
Currently, electroencephalogram (EEG) provides critical data to support the diagnosis of
epilepsy through the identification of seizure events. The review process is undertaken by …
epilepsy through the identification of seizure events. The review process is undertaken by …
Pediatric and Adolescent Seizure Detection: A Machine Learning Approach Exploring the Influence of Age and Sex in Electroencephalogram Analysis
Background: Epilepsy, a prevalent neurological disorder characterized by recurrent seizures
affecting an estimated 70 million people worldwide, poses a significant diagnostic …
affecting an estimated 70 million people worldwide, poses a significant diagnostic …
Multimodal EEG-fNIRS Seizure Pattern Decoding Using Vision Transformer
Epilepsy has been analyzed through uni-modality non-invasive brain measurements such
as electroencephalogram (EEG) signal, but identifying seizure patterns is more challenging …
as electroencephalogram (EEG) signal, but identifying seizure patterns is more challenging …
[PDF][PDF] A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification.
Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for
diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly …
diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly …
Optimal Graph Representations and Neural Networks for Seizure Detection Using Intracranial EEG Data
In recent years, several machine-learning (ML) solutions have been proposed to solve the
problems of seizure detection, seizure characterization, seizure prediction, and seizure …
problems of seizure detection, seizure characterization, seizure prediction, and seizure …
Emergence of high-connectivity states before epileptic seizures: Multi-patient validation, physiological correlates, and network modeling
N Medina, M Vila-Vidal, A Tost, M Khawaja, M Carreño… - bioRxiv, 2025 - biorxiv.org
Epilepsy affects approximately 50 million people worldwide, making it one of the most
prevalent neurological disorders. Predicting seizures through reliable pre-seizure …
prevalent neurological disorders. Predicting seizures through reliable pre-seizure …