Latest AI and machine learning research in seizures for healthcare professionals.
Attention deficit hyperactivity disorder is a prevalent syndrome that costs billions of dollars annually. Finding meaningful interventions based upon predictive baseline EEG values can reduce uncertainty in symptom remediation. This study aims to deepen the understanding of ADHD neurophysiology and contribute to the development of personalized approaches in its treatment. This study retrospectivel...
OBJECTIVE: This study investigated EEG microstate dynamics in trigeminal neuralgia (TN) patients to understand the central nervous system's contribution to this neuropathic pain condition. Despite TN's traditional classification as a peripheral neuropathy, altered brain network organization may play a critical role in pain chronification and treatment resistance, making EEG microstates a valuable ...
BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagno...
OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in m...
OBJECTIVE: Seizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of ...
Nicotine abstinence inhibits the function of the mesolimbic dopamine system to enhance craving. EEG microstates may provide spatiotemporal characteris...
Cortico-cortical evoked potentials (CCEPs) are an active electrophysiological technique used during intracranial electroencephalography to evaluate th...
Speech imagery-the process of mentally simulating speech without vocalization-is a promising approach for brain-computer interfaces (BCIs), enabling a...
Electroencephalography (EEG) is a widely used tool for monitoring brain activity, but it is often disturbed by various artifacts, such as electrooculo...
Automatic sleep staging from single-channel electroencephalography (EEG) using artificial intelligence (AI) is emerging as an alternative to costly an...
Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tac...
BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperativ...
The utilization of large language model-based artificial intelligence (AI) in the field of neurology has gained attention as a viable tool to enhance ...
In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-bas...
Non-suicidal self-injury (NSSI) is a serious threat to the physical and mental health of adolescents, significantly increasing the risk of suicide and...
Drug-resistant epilepsy (DRE) patients typically require surgical intervention or neurostimulation. Therefore, accurate localization of the seizure on...
Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering s...
Status epilepticus (SE), seizures lasting beyond five minutes, is a medical emergency commonly treated with benzodiazepines which enhance GABA recepto...
This work describes a dataset containing high-density EEG (hd-EEG) and surface electromiography (sEMG) to capture neuromechanical responses during a r...
INTRODUCTION: Affecting millions of individuals worldwide, epilepsy is a neurological condition marked by repeated convulsions. Monitoring brain activ...