Feature clustering for robust frequency-domain classification of EEG activity

Journal of Neuroscience Methods
Andrew Myrden, Tom Chau

Abstract

The analysis of electroencephalograms is often performed in the frequency-domain. These analyses typically involve the computation of spectral power either over pre-defined frequency bands (e.g. theta, delta, alpha, and beta bands) or over a large number of narrow frequency ranges. However, the former technique ignores variability in these frequency bands over time and between participants while the latter ignores the significant redundancy between these powers. This paper details an unsupervised feature extraction method for EEG data that uses a clustering of features to agglomerate narrow-band spectral powers based on their similarities. This method computes a set of analogues to the traditional frequency bands that are data-driven and participant-specific. A fast correlation-based filter was used to identify which of these agglomerated features were most useful for each investigated classification problem. The new feature clustering algorithm was used to detect changes in three mental states and to detect the performance of three mental tasks. Balanced classification accuracies approaching or exceeding 70% were attained for all classification problems. Classification accuracies attained by this algorithm were compared to tho...Continue Reading

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Jun 18, 2015·Frontiers in Human Neuroscience·Andrew Myrden, Tom Chau

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Citations

Jan 17, 2017·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Andrew Myrden, Tom Chau
Apr 24, 2020·Journal of Speech, Language, and Hearing Research : JSLHR·Ellyn A Riley, Arthur Owora
Jun 6, 2020·International Journal of Neural Systems·Filip Stojic, Tom Chau
Nov 15, 2016·Journal of Neural Engineering·A Myrden, T Chau

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