Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
Sensors (Basel). 2022 Apr 15;22(8):3048. doi: 10.3390/s22083048.
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. The goal of this work is to outline an objective strategy for discovering optimal EEG bands based on signal power spectra. A two-step data-driven methodology is presented for objectively determining the best EEG bands for a given dataset. 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. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. Additionally, key spectral components were isolated in dedicated frequency bands. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.
脑电图（EEG）是一种在头皮上放置电极的脑成像技术。EEG 信号通常被分解为称为 delta、theta、alpha 和 beta 的频带。虽然这些频带已被证明对表征各种脑状态有用，但它们作为一种一刀切的分析工具的效用仍不清楚。这项工作的目的是概述一种基于信号功率谱来发现最佳 EEG 频带的客观策略。提出了一种两步数据驱动方法，用于客观确定给定数据集的最佳 EEG 频带。首先，使用决策树来估计再现信号功率谱的最佳频带边界，用于预定数量的频带。然后，使用基于 Akaike 信息准则（AIC）的质量分数来确定最佳频带数量，该质量分数平衡了拟合优度和频带数量少。通过确定表现优于更常用频带边界两倍的频带，这种数据驱动方法更好地描述了潜在的功率谱。此外，在专用频带中分离了关键的光谱分量。该方法提供了一种全自动且灵活的方法来捕获关键信号分量，并可能发现新的大脑活动指标。
