Anxiety Classification Using an InformationGeometry–Driven Fuzzy Neural Network

This is the authors' abstract. We don't add key points for this paper.

Information geometry is a contemporary geometric exploration of the universe of information. Presently,informational theories have primarily been researched using algebraic, logical, analytical, and probabilistic approaches.Geometry, which analyses reciprocal relationships between components such as distance and curvature, could givemajor advantages to the science of information. In this study, we proposed a novel model for EEG signals classificationbased on a Riemannian space fuzzy neural network. The sample covariance matrix is utilized as an EEG signalcharacteristic in this suggested approach, and Riemannian mean and distance are used to categorize the matrix,therefore achieving the usage of manifold learning. The experimental findings indicate that this procedure is moresuccessful and precise than earlier methods for recognizing messages from brain waves.