Data-Driven EEG Band Discovery with Decision Trees Article Swipe
Shawhin Talebi
,
John Waczak
,
Bharana Fernando
,
Arjun Sridhar
,
David J. Lary
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22083048
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/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.
Related Topics
Concepts
Electroencephalography
Akaike information criterion
Spectral density
Computer science
Radio spectrum
Pattern recognition (psychology)
Artificial intelligence
SIGNAL (programming language)
Frequency band
Decision tree
Alpha (finance)
Speech recognition
Mathematics
Machine learning
Statistics
Bandwidth (computing)
Psychology
Telecommunications
Construct validity
Psychiatry
Programming language
Psychometrics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22083048
- https://www.mdpi.com/1424-8220/22/8/3048/pdf?version=1650019953
- OA Status
- gold
- Cited By
- 10
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224298655
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4224298655Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22083048Digital Object Identifier
- Title
-
Data-Driven EEG Band Discovery with Decision TreesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-15Full publication date if available
- Authors
-
Shawhin Talebi, John Waczak, Bharana Fernando, Arjun Sridhar, David J. LaryList of authors in order
- Landing page
-
https://doi.org/10.3390/s22083048Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/8/3048/pdf?version=1650019953Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/8/3048/pdf?version=1650019953Direct OA link when available
- Concepts
-
Electroencephalography, Akaike information criterion, Spectral density, Computer science, Radio spectrum, Pattern recognition (psychology), Artificial intelligence, SIGNAL (programming language), Frequency band, Decision tree, Alpha (finance), Speech recognition, Mathematics, Machine learning, Statistics, Bandwidth (computing), Psychology, Telecommunications, Construct validity, Psychiatry, Programming language, PsychometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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