arXiv:2607. 19429v1 Announce Type: new Abstract: Electroencephalographic (EEG) abnormalities arise from dynamic changes in neural synchrony across spatial and temporal scales, yet many computational approaches reduce these dynamics to static features.
arXiv:2602. 11801v2 Announce Type: replace Abstract: Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics.
arXiv:2607. 14314v1 Announce Type: new Abstract: Seizure diagnosis from EEG signals is a critical yet persistently challenging task, due to the complicated neural dynamics and the spurious connections in inter-channel modeling.
arXiv:2510. 11917v2 Announce Type: replace Abstract: Dementia disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in EEG that challenge accurate diagnosis.
Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channels. Conditional flow matching...
arXiv:2609.37934v1 Announce Type: new Abstract: Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured varia...
arXiv:2607. 02063v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have been widely used to capture spatial functional connectivity patterns to improve electroencephalography (EEG)-based depression recognition performance.
arXiv:2608.20380v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided di...
arXiv:2608. 14847v1 Announce Type: new Abstract: Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases.
arXiv:2604. 00163v2 Announce Type: replace-cross Abstract: Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events.
BrainNet Studio is a unified toolkit that enables the construction, analysis, and visualization of both static and dynamic brain networks. It integrates 27 algorithms—including deep learning, graph neural networks, and spatiotemporal sequence models—to support classification, biomarker identification, and the extraction of discriminative brain regions and connections. The toolkit also employs a large language model to generate researcher‑verifiable summaries of functional and structural connectivity, as well as structure‑function coupling, at individual and group levels.
arXiv:2608.29304v1 Announce Type: new Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...
arXiv:2609.37220v1 Announce Type: new Abstract: Exploring high-order correlations and long-range dependencies in brain networks holds significant value for both neuroscience research and clinical dia...