Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces

• URL: http://arxiv.org/abs/2504.05534v1
• Date: Mon, 07 Apr 2025 22:11:59 GMT
• Title: Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces
• Authors: Arnau Marin-Llobet, Arnau Manasanch, Sergio Sanchez-Manso, Lluc Tresserras, Xinhe Zhang, Yining Hua, Hao Zhao, Melody Torao-Angosto, Maria V Sanchez-Vives, Leonardo Dalla Porta,
• Abstract summary: We propose a new method for brain decoding using invasive electrophysiological recordings.<n>The method achieves a superior mean F1 macro-averaged score across different channel configurations.<n>The geometric framework reveals distinct spatial contributions of brain regions across varying brain states.
• Score: 3.0026377736031846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the application of Riemannian geometry-based methods for brain decoding using invasive electrophysiological recordings. Although previously employed in non-invasive, the utility of Riemannian geometry for invasive datasets, which are typically smaller and scarcer, remains less explored. Here, we propose a Minimum Distance to Mean (MDM) classifier 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, we evaluated the performance of our approach against Convolutional Neural Networks (CNNs) and Euclidean MDM classifiers. Our 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. Our findings align with previous studies supporting the efficacy of geometry-based methods and extending their application to invasive brain recordings, highlighting their potential for broader clinical use, such as brain computer interface applications.

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