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Investigation of human brain states through electroencephalograph (EEG)signals is a crucial step in human-machine communications. However, classifyingand analyzing EEG signals are challenging due to their noisy, nonlinear andnonstationary nature. Current methodologies for analyzing these signals oftenfall short because they have several regularity assumptions baked in. This workprovides an effective, flexible and noise-resilient scheme to analyze EEG byextracting pertinent information while abiding by the 3N (noisy, nonlinear andnonstationary) nature of data. We implement a topological tool, namelypersistent homology, that tracks the evolution of topological features overtime intervals and incorporates individual's expectations as prior knowledge bymeans of a Bayesian framework to compute posterior distributions. Relying onthese posterior distributions, we apply Bayes factor classification to noisyEEG measurements. The performance of this Bayesian classification scheme isthen compared with other existing methods for EEG signals.

Bayesian Topological Learning for Brain State Classification