  Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic
oscillatory components (DMs). DMs can improve the accuracy of neural decoding
when used with the nonlinear Grassmann kernel, compared to conventional power
features. However, such kernel-based machine learning algorithms have three
limitations: large computational time preventing real-time application,
incompatibility with non-kernel algorithms, and low interpretability. Here, we
propose a mapping function corresponding to the Grassmann kernel that
explicitly transforms DMs into spatial DM (sDM) features, which can be used in
any machine learning algorithm. Using electrocorticographic signals recorded
during various movement and visual perception tasks, the sDM features were
shown to improve the decoding accuracy and computational time compared to
conventional methods. Furthermore, the components of the sDM features
informative for decoding showed similar characteristics to the high-$\gamma$
power of the signals, but with higher trial-to-trial reproducibility. The
proposed sDM features enable fast, accurate, and interpretable neural decoding.
