Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

This study investigates the application of Riemannian geometry‐based methods for brain decoding using invasive electrophysiological recordings. While Riemannian geometry has been successfully applied in noninvasive settings, its utility for invasive datasets, which are typically smaller and scarcer, remains less explored. Herein, a minimum distance to mean (MDM) classifier is proposed using a Riemannian geometry approach based on covariance matrices extracted from intracortical local field potential (LFP) recordings across various regions during different brain state dynamics. For benchmarking, the performance of the approach is evaluated against convolutional neural networks (CNNs) and Euclidean MDM classifiers. The results indicate that the Riemannian geometry‐based classification not only achieves a superior mean F1 macro‐averaged score across different channel configurations but also requires up to two orders of magnitude less computational training time. Additionally, the geometric framework reveals distinct spatial contributions of brain regions across varying brain states, suggesting a state‐dependent organization that traditional time series‐based methods often fail to capture. The findings align with previous studies supporting the efficacy of geometry‐based methods and extend their application to invasive brain recordings, highlighting their potential for broader clinical use, such as brain‐computer interface applications.