Latest AI and machine learning research in seizures for healthcare professionals.
Learning transferable representations for electroencephalography (EEG) remains challenging because EEG signals are inherently multi-channel and non-stationary. Channels observed at the same time provide coupled measurements of neural activity, while the relevant temporal dynamics vary across contexts. This structure is poorly matched by architectures that apply uniform computation across time or r...
Syntaxin-binding protein 1 (STXBP1) mutations lead to severe epilepsy, intellectual disability, developmental delay, and movement disorder. Effective treatments for these conditions do not exist. Recent studies in Munc18-1 (STXBP1) C.elegans models demonstrate that 4-phenylbutyrate (4-PBA) or related pharmacological chaperones stabilize Munc18-1 protein levels and rescue locomotion deficits. These...
Motor imagery electroencephalography (MI-EEG) decoding offers a non-invasive route for post-stroke rehabilitation, but cross-patient use remains diffi...
Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned t...
This study aims to predict human intentions during intense sports activities, specifically in table tennis. Using a publicly available Real World Tabl...
Distinct smartphone interaction behaviors, like short-form video scrolling and mobile gaming, elicit qualitatively different cognitive and physiologic...
Importance: Implantable sub-scalp EEG systems with a small number of channels have emerged as promising solutions for long-term seizure monitoring in ...
Electroencephalography (EEG) interpretation in clinical practice relies on the analysis of energy distribution across standard frequency bands. The We...
Background: Integrating multimodal data into medical artificial intelligence (AI) tools and evaluating whether they outperform human experts remains a...
Background and Objectives: Electrical stimulation mapping (ESM) is the clinical gold standard for identifying eloquent cortex during presurgical evalu...
Multimodal Large Language Models (MLLMs) have demonstrated robust capabilities in recognizing everyday human activities, yet their potential for analy...
Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how th...
Recent EEG-to-image retrieval methods leverage pretrained vision encoders and foveation-inspired priors, but typically assume a fixed, center-focused ...
Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training an...
Brain encoding models not only serve to decipher how visual stimuli are transformed into neural responses, but also represent a critical step toward v...
Developing robust and clinically reliable EEG biomarkers requires evaluation frameworks that explicitly address cross population generalization in mul...
Patients with dementia typically exhibit cognitive impairment, which is routinely assessed using the Mini-Mental State Examination (MMSE). Concurrentl...
Electroencephalography (EEG) is highly susceptible to artifact contamination, such as electrooculographic (EOG) and electromyographic (EMG) interferen...
Spontaneous thoughts constitute most of everyday inner experience, yet long-standing methodological challenges obscure a thorough exploration of their...
Objective: Decoding visual information from electroencephalography (EEG) is an important problem in neuroscience and brain-computer interface (BCI) re...