Latest AI and machine learning research in seizures for healthcare professionals.
Transcranial direct current stimulation (tDCS) as a non-invasive stimulation is still in the experimental stage for many psychiatric disorders even in adults. The use of tDCS provides an opportunity to influence neural networks and their functional connectivity. How tDCS affects cortical networks and how it influences the functional connectivity of the developing brain is largely unknown. The elec...
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes remains underexplored.
BACKGROUND: Epilepsy is a multifaceted neurological disorder marked by seizures that can present with a wide range of symptoms. Despite the prevalent ...
INTRODUCTION: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acqu...
Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions....
Attention deficit hyperactivity disorder is a prevalent syndrome that costs billions of dollars annually. Finding meaningful interventions based upon ...
OBJECTIVE: This study investigated EEG microstate dynamics in trigeminal neuralgia (TN) patients to understand the central nervous system's contributi...
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...