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.

翻译：在脑机接口（BCI）领域，脑信号的解读与内省对于提供反馈或指导快速范式原型设计至关重要，但由于信号的高噪声水平和高维度特性，这一过程极具挑战性。深度神经网络通常通过将学习到的特征表示转换为二维或三维子空间可视化（例如利用均匀流形逼近与投影算法）来进行内省分析。然而，此类方法计算成本高昂，使得数据流的实时投影成为一项艰巨任务。本研究提出了一种名为近似UMAP（aUMAP）的新型UMAP变体，旨在生成可供实时内省的快速投影。为评估其实时投影的适用性，我们将其与标准UMAP及其神经网络对应方法参数化UMAP进行了基准测试。结果表明，近似UMAP在保持相同训练时间的同时，生成的投影不仅复现了标准UMAP的投影空间，还将投影速度提升了一个数量级。