Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Data-Driven EEG Band Discovery with Decision Trees

Version 1 : Received: 7 March 2022 / Approved: 10 March 2022 / Online: 10 March 2022 (10:38:44 CET)

A peer-reviewed article of this Preprint also exists.

Talebi, S.; Waczak, J.; Fernando, B.A.; Sridhar, A.; Lary, D.J. Data-Driven EEG Band Discovery with Decision Trees. Sensors 2022, 22, 3048. Talebi, S.; Waczak, J.; Fernando, B.A.; Sridhar, A.; Lary, D.J. Data-Driven EEG Band Discovery with Decision Trees. Sensors 2022, 22, 3048.

Journal reference: Sensors 2022, 22, 3048
DOI: 10.3390/s22083048

Abstract

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. We present a two-part data-driven methodology for objectively determining the best EEG bands for a given dataset in this paper. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. Data-driven EEG band discovery may aid in objectively capturing key signal components and uncovering new indices of brain activity.

Keywords

electroencephalography (EEG); EEG bands; decision tree; machine learning

Subject

MEDICINE & PHARMACOLOGY, Behavioral Neuroscience

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