Anxiety Classification Using an Information Geometry–Driven Fuzzy Neural Network

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 give major advantages to the science of information. In this study, we proposed a novel model for EEG signals classification based on a Riemannian space fuzzy neural network. The sample covariance matrix is utilized as an EEG signal characteristic 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 more successful and precise than earlier methods for recognizing messages from brain waves.