Application of identity vectors for EEG classification

Abstract

BackgroundFinding an optimal EEG subject verification algorithm is a long standing goal within the EEG community. For every advancement made, another feature set, classifier, or dataset is often introduced; tracking improvements in classification without a consistent benchmark, such as a classifier-feature pairing tested on a publicly available dataset, makes it difficult to understand how and why these improvements occur. New MethodFollowing on previous biometric experiments, I-Vectors and Gaussian Mixture Model-Universal Background Models are compared to an established Mahalanobis classifier. A second experiment then addresses the impact of epoch duration as a function of classification performance across all three classifiers. ResultsThe experimental classification results indicate that I-Vectors are more robust than the other classifiers displaying less sensitivity to epoch duration, data composition, and feature selection. Comparison with Existing MethodsThis I-Vector based approach is compared against commonly used EEG classifiers, such as Mahalanobis and Gaussian mixture models. These classifiers are benchmarked using the publicly available PhysioNet database converted into three feature sets, spectral coherence, power spectral density, and cepstral coefficients. ConclusionsThe experimental results suggests I-Vectors provide reliable baseline performance by leveling the field between feature set and datasets making them well suited for EEG signal processing tasks.