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Spatial patterns underlying population differences in the background EEG
Abstract
A method is described which can be used to extract common spatial patterns underlying the EEGs from two human populations. These spatial patterns account, in the least-squares sense, maximally for the variance in the EEGs from one population and minimally for the variance in the other population and therefore would seem to be optimal for quantitatively discriminating between the individual EEGs in the two populations. By using this method, it is suggested that the problems associated with the more common approach to discriminating EEGs, significance probability mapping, can be avoided. The method is tested using EEGs from a population of normal subjects and using the EEGs from a population of patients with neurologic disorders. The results in most cases are excellent and the misclassification which occurs in some cases is attributed to the nonhomogeneity of the patient population particularly. The advantages of the method for feature selection, for automatically classifying the clinical EEG, and with respect to the reference-free nature of the selected features are discussed.
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“Kang et al. [13] and Lotte and Guan [19] improved covariance matrix estimation for CSP filters by regularizing it towards the average of other subjects, or constructing a common feature space.”
From Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2020) · cites this paper as [19]
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Metadata
- Authors
- Koles, Z. J., Lazar, M. S., & Zhou, S. Z.
- Affiliations
- University of Alberta (CA)
- Venue
- Brain Topography
- Published
- 1990
- Volume
- 2
- Issue
- 4
- Pages
- 275-284
- ISSN
- 0896-0267 (ISSN-L)
- 1573-6792
- OpenAlex
- W2082070408
- Cited by
- 364
- References
- 12
- Citations by year
- 2026: 9 · 2025: 34 · 2024: 33 · 2023: 32 · 2022: 33 · 2021: 27 · 2020: 29 · 2019: 26 · 2018: 24 · 2017: 20 · 2016: 17 · 2015: 10 · 2014: 15 · 2013: 5 · 2012: 9
- FWCI
- 0.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Brain Topography (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- common spatial pattern, EEG classification, population differences, EEG background activity, feature selection, neurological disorders, misclassification
- MeSH terms
- Adolescent, Adult, Analysis of Variance, Brain, Brain Diseases, Brain Mapping, Electrodes, Electroencephalography, Female, Humans, Male, Middle Aged, Reference Values
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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