Research Background and Purpose Epilepsy is a spontaneous and severe neurological disorder characterized by recurrent seizures, affecting approximately 50 million people worldwide [1]. Despite recent advances in anti-seizure medications (ASMs), drug-resistant epilepsy (DRE) still affects 20% to 30% of epilepsy patients [1-3]. DRE patients face sign...

Brain Functional Network for Anxiety Analysis and Detection Based on Improved Ensemble Empirical Mode Decomposition Academic Background and Research Objectives With the increasing stress of modern life, anxiety, a common neurological disorder, has become an urgent issue in global public health. Anxiety not only manifests as mental disorders but als...

The Role of EEG Microstates in Predicting the Therapeutic Outcomes of Oxcarbazepine in Newly Diagnosed Focal Epilepsy Patients Introduction Background Focal epilepsy is the most common type of epilepsy, accounting for about 60% of all epilepsy cases. The selection of antiepileptic drugs (AEDs) varies depending on the type of epilepsy. In the treatm...

Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in MRI Background Glioma is the most common malignant primary brain tumor in adults. According to the 2021 World Health Organization (WHO) classification of tumors, genotype plays a significant role in the classification of tumor subtypes, especially the isocitrate dehy...

In modern medical diagnostics and clinical research, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) technology provides significant information regarding tissue pathophysiology. By fitting a Tracer-Kinetic (TK) model, pharmacokinetic (PK) parameters can be extracted from time-series MRI signals. However, these estimated PK parameter...

Classification of 3D MRI Gliomas and Alzheimer’s Disease Based on the Siamese-Transport Domain Adaptation Framework Background In computer-aided diagnosis, 3D magnetic resonance imaging (MRI) screening plays a vital role in the early diagnosis of various brain diseases, effectively preventing the deterioration of the condition. Glioma is a common m...

DeepSleepNet: An Automatic Sleep Stage Scoring Model Based on Single-Channel EEG Background Introduction Sleep has a significant impact on human health, and monitoring sleep quality is crucial in medical research and practice. Typically, sleep experts score sleep stages by analyzing various physiological signals such as electroencephalogram (EEG), ...

In recent years, Virtual Reality (VR) technology has become increasingly common, with related hardware becoming more affordable. For example, current head-mounted displays (HMDs) on the market not only offer high-resolution displays but also feature precise head and handheld controller tracking. Initially, these technologies were mostly used in the...

In recent years, due to the high incidence and lethality of brain tumors, rapid and accurate detection and classification of brain tumors have become particularly important. Brain tumors include both malignant and non-malignant types, and their abnormal growth can cause long-term damage to the brain. Magnetic Resonance Imaging (MRI) is a commonly u...

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. However, existing classification methods still face numerous challenges in handling the spatial and temporal features of time-varying multi-channel brain signals, coping with individual differences in biological signals, and model interpretability. Traditional...