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

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 in real-time a non-trivial task. In this study, we introduce a novel variant of UMAP, called approximate UMAP (aUMAP). It aims at generating rapid projections for real-time introspection. To study its suitability for real-time projecting, we benchmark the methods against standard UMAP and its neural network counterpart parametric UMAP. Our results show that approximate UMAP delivers projections that replicate the projection space of standard UMAP while decreasing projection speed by an order of magnitude and maintaining the same training time.