Latest AI and machine learning research in seizures for healthcare professionals.
Brain-state-guided and closed-loop transcranial magnetic stimulation (TMS) protocols have emerged as...
SCN2A-related disorders result from pathogenic variants in the gene encoding for the voltage-gated s...
The internal representations of large language models (LLMs) correlate, or "align" , with human neur...
Foundation models (FMs) promise to extract unified representations that generalize across downstream...
Mild traumatic brain injury (mTBI) is a prevalent condition that remains difficult to diagnose in it...
The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve p...
Learning transferable representations for electroencephalography (EEG) remains challenging because E...
Syntaxin-binding protein 1 (STXBP1) mutations lead to severe epilepsy, intellectual disability, deve...
Motor imagery electroencephalography (MI-EEG) decoding offers a non-invasive route for post-stroke r...
Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in...
This study aims to predict human intentions during intense sports activities, specifically in table ...
Distinct smartphone interaction behaviors, like short-form video scrolling and mobile gaming, elicit...
Importance: Implantable sub-scalp EEG systems with a small number of channels have emerged as promis...
Electroencephalography (EEG) interpretation in clinical practice relies on the analysis of energy di...
Background: Integrating multimodal data into medical artificial intelligence (AI) tools and evaluati...
Background and Objectives: Electrical stimulation mapping (ESM) is the clinical gold standard for id...
Multimodal Large Language Models (MLLMs) have demonstrated robust capabilities in recognizing everyd...
Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their e...
Recent EEG-to-image retrieval methods leverage pretrained vision encoders and foveation-inspired pri...
Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which in...
Brain encoding models not only serve to decipher how visual stimuli are transformed into neural resp...