Latest AI and machine learning research in seizures for healthcare professionals.
BACKGROUND AND OBJECTIVE: Electroencephalography (EEG) is a crucial tool for monitoring recovery in patients with disorders of consciousness (DOC) after therapeutic interventions. It helps in identifying the neural correlates and in guiding the development of personalized treatments. Spectrum power measures are widely employed. However, these measures are manually handcrafted, not patient-specific...
Electroencephalography (EEG)-based visual decoding has significant potential in brain-computer interfaces but faces substantial challenges due to noise, inter-subject variability, and limited fine-grained alignment between neural signals and visual representations. Existing approaches predominantly utilize global EEG embeddings and static fusion methods, restricting their capability to capture nua...
BACKGROUND: One of the primary objectives of neuroscience is to gather information from the brain. Therefore, brain data are crucial for understanding...
Epilepsy is a highly prevalent chronic central nervous system disorder that imposes substantial societal and economic burdens. Inconsistent associatio...
Driver fatigue poses a severe risk to road safety, contributing to approximately 20% of fatal accidents worldwide. While EEG signals are the gold stan...
BACKGROUND AND OBJECTIVE: Recognizing pilot operational intent is crucial for enhancing flight safety and improving the efficiency of human-machine in...
BACKGROUND: Atypical depression (AD) is a distinct subtype of depression, with interpersonal sensitivity as one of its core characteristics. However, ...
This study proposes a novel adaptive DBS control strategy for epilepsy treatment based on deep reinforcement learning. By establishing a random distur...
Neuroscientific investigations have revealed the presence of regional pathway connections among functional brain areas, as well as the asymmetrical st...
BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography ...
BACKGROUND: Depression is a major public health concern, with a rising prevalence among adolescents and young adults. However, the neural mechanisms u...
Epilepsy is a neurological disorder characterized by transient and recurrent abnormal brain activity, often diagnosed through manual inspection extens...
BACKGROUND: Electroencephalogram (EEG) microstates reflect momentary localized brain activity and may indicate spontaneous fluctuations within large-s...
Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the uniq...
BACKGROUND: Opioid addiction is a major public health concern, associated with numerous health and social problems. Conventional diagnostic methods fo...
Deep learning has shown promise in motor imagery-based electroencephalogram (MI-EEG) decoding, a critical task in non-invasive brain-computer interfac...
This study aims to develop a multimodal driver emotion recognition system that accurately identifies a driver's emotional state during the driving pro...
The intricate and efficient information processing of the human brain, driven by spiking neural interactions, has led to the development of spiking ne...
Focal cortical dysplasia (FCD) is a neurodevelopmental malformation that often manifests as medically refractory epilepsy. A key histological hallmark...
BACKGROUND: Flexible wearable medical devices drive healthcare transformation via non-invasive, real-time physiological monitoring and personalized ma...