Latest AI and machine learning research in seizures for healthcare professionals.
Deep learning (DL) models have achieved impressive performance in EEG-based prediction tasks, but they often lack interpretability, limiting their clinical utility. In this study, we introduce a novel self-supervised learning (SSL) framework inspired by neurophysiological reactivity. Our approach models healthy EEG transitions between ocular states by predicting an EEG-derived feature under eyes-o...
Human perception is robust under challenging conditions, for example when sensory inputs change over time. Temporal adaptation in the form of reduced responses to repeated external stimuli is ubiquitously observed in the brain, yet it remains unclear how repetition suppression aids recognition of novel inputs. To clarify this, we collected behavioural and electrocorticography (EEG) measurements wh...
Accurate identification of the seizure onset zone (SOZ) using intracranial electroencephalography (iEEG) remains challenging. Although diverse methods...
Deep learning has emerged as a powerful tool for extracting meaningful patterns from electroencephalography (EEG) signals, particularly for mental wor...
Deep neural networks applied to signal processing tasks often need specialized architectural mechanisms to capture the temporal history of input signa...
Beta bursts are brief, transient increases in beta-band (13–30 Hz) EEG activity that play a key role in motor control, particularly in processes like ...
When we listen to music, we often feel a pleasurable urge to move to music, known as groove. While previous studies have identified musical features t...
fMRI signals were traditionally seen as slow and sampled in the order of seconds, but recent technological advances have enabled much faster sampling ...
Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts severe pressure on healthcare systems. Early detection...
Physiological artifacts pose persistent challenges in electroencephalo-gram (EEG) data acquisition, often compromising interpretation and post-analysi...
Sleep and circadian rhythms both contribute to cognitive performance, but the underlying neuronal network-level changes remain unclear. We quantified ...
Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, rapidly impairs communication within years of onset. This loss ...
An electroencephalogram (EEG) is an electrical measurement of brain activity using electrodes placed on the scalp surface. After EEG measurements are ...
Automated seizure detection in animal electroencephalography (EEG) is crucial for accelerating epilepsy research. While machine learning (ML) and deep...
Understanding how the brain represents information is a central challenge in neuroscience and a practical bottleneck for brain-computer interfaces. Ex...
Interventions supporting medical care and enhancing quality of life in neurodegenerative or age-related cognitive decline are strongly needed. Electro...
Major depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Unders...
This study presents a deep probabilistic spiking neural network designed to extract discriminative spatiotemporal features from EEG signals associated...
Neural interfaces are essential tools for diagnosing and managing neurological disorders, yet conventional electrocorticography (ECoG) devices are lim...
Taste perception is central to flavor experiences. Electroencephalography (EEG) signals carry rich information about taste perception. Because these n...