Latest AI and machine learning research in seizures for healthcare professionals.
Human visual reconstruction aims to reconstruct fine-grained visual stimuli based on subject-provided descriptions and corresponding neural signals. As a widely adopted modality, Electroencephalography (EEG) captures rich visual cognition information, encompassing complex spatial relationships and chromatic details within scenes. However, current approaches are deeply coupled with an alignment fra...
Machine learning methods employing neuroimaging data are useful for monitoring the activation of neural representations. Specifically, they can be used to discern the brain networks engaged in processing specific categories of items. This approach has been used predominantly with functional magnetic resonance imaging data, and more rarely with electroencephalography (EEG) data. Here, we present a ...
Electroencephalography (EEG) is a widely used tool for studying brain function, with applications in clinical neuroscience, diagnosis, and brain-compu...
Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and...
Persistent homology (PH) -- the conventional method in topological data analysis -- is computationally expensive, requires further vectorization of it...
Visual stimuli reconstruction from EEG remains challenging due to fidelity loss and representation shift. We propose CognitionCapturerPro, an enhanced...
The use of wearables in medicine and wellness, enabled by AI-based models, offers tremendous potential for real-time monitoring and interpretable even...
Epileptic seizure forecasting is a clinically important yet challenging problem in epilepsy research. Existing approaches predominantly rely on neural...
Electroencephalogram (EEG) classification is critical for applications ranging from medical diagnostics to brain-computer interfaces, yet it remains c...
Electroencephalography (EEG) provides a non-invasive window into neural dynamics at high temporal resolution and plays a pivotal role in clinical neur...
We present an EEG-based approach to characterize disease-related spectro-temporal signatures in Alzheimer's disease (AD) and Parkinson's disease (PD)....
Accurate detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG) plays a crucial role in epilepsy diagnosis. Our work i...
Magnetoencephalography (MEG) and electroencephalography (EEG) source imaging requires solving an ill-posed inverse problem for which numerous algorith...
Motor imagery (MI) brain-computer interfaces (BCIs) are promising technologies for neurorehabilitation. In this context, deep learning (DL) models are...
Resting-state electroencephalography (EEG) has been proposed as a scalable source of biomarkers for chronic pain, but its clinical potential remains u...
Sense of agency (SoA), the experience of controlling one's actions and their consequences, is crucial for self-representation and adaptive goal-direct...
Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely ada...
Objective Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on po...
Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited to a si...
Importance: Tracking and predicting seizure frequency in patients with epilepsy is important for prognostication and therapy management. Interictal sp...