Accurate segmentation of brain vessels in magnetic resonance angiography (MRA) is essential for surgical procedures. Neural networks are powerful tools for medical image segmentation, but their development requires well-annotated datasets. However, publicly available MRA datasets with detailed vessel annotations are scarce. We present a dataset of 100 manually annotated brain MRA images from the IXI Dataset, representing one of the largest publicly available collections with detailed vessel segmentation. We focused on the Circle of Willis and associated vessels, critical for neurovascular surgery planning. The annotation pipeline involved automated segmentation using the Frangi vesselness filter, followed by manual refinement by three annotators under supervision of three neurovascular surgeons. Images were acquired using 1.5T and 3T MRI scanners. The dataset includes demographic metadata, with clustering analysis revealing four distinct morphological patterns. This resource enables development of automated segmentation algorithms, investigation of cerebrovascular morphology variations, and advancement of AI-driven diagnostic tools.

EEG-based BCIs face noise and computational constraints for on-device deployment. We present a lightweight pipeline using Hankel embeddings: projecting EEG blocks onto paired eigenvectors yields oscillators-closed-orbit components. Our approach provides three contributions: (1) jPCA-based oscillator dynamics with mechanics-inspired descriptors for stable, class-separable features; (2) adaptive band-pass filtering by reconstructing signals from selected oscillators, suppressing noise without preset frequencies; (3) consistent spatiotemporal oscillator localization across channels. The pipeline uses only matrix operations, enabling GPU-free streaming. On motor-imagery benchmarks, our Hankel-based filter improves classifier accuracy by 6%, providing a resource-efficient EEG representation for edge BCI systems.