Approximate UMAP allows for high-rate online visualization of high-dimensional data streams

arXiv:2404.04001v1 Announce Type: new
Abstract: In the BCI field, introspection and interpretation of brain signals are desired for providing feedback or to guide rapid paradigm prototyping but are challenging due to the high noise level and dimensionality of the signals. Deep neural networks are often introspected by transforming their learned feature representations into 2- or 3-dimensional subspace visualizations using projection algorithms like Uniform Manifold Approximation and Projection (UMAP). Unfortunately, these methods are computationally expensive, making the projection of data streams …

abstract arxiv bci brain brain signals cs.ai cs.hc cs.lg data data streams dimensionality eess.sp feedback guide interpretation networks neural networks noise paradigm prototyping rate type umap visualization