The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
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]
Citations
Cites
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,023
Introduction to Statistical Pattern Recognition
Webb, A. R., & Copsey, K. D. (2011) · doi:10.1002/9781119952954.ch1 · Cited by 1,607
Principles of spatial analysis
Lehmann, D. (1987) · Medical Entomology and Zoology · Cited by 494
Scalp Current Density Mapping: Value and Estimation from Potential Data
Perrin, F., Bertrand, O., & Pernier, J. (1987) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1987.326089 · Cited by 425
Significance probability mapping: An aid in the topographic analysis of brain electrical activity
Duffy, F. H., Bartels, P. H., & Burchfiel, J. L. (1981) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(81)90221-2 · Cited by 354
Quantitative EEG: I. Techniques and problems of frequency analysis and topographic mapping.
Nuwer, M. R. (1988) · PubMed · Cited by 254
Nuwer, M. R. (1988) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-198801000-00001 · Cited by 247
Analysis of the Electromagnetic Signals of the Human Brain: Milestones, Obstacles, and Goals
Gevins, A. S. (1984) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1984.325246 · Cited by 127
Nuwer, M. R. (1988) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-198801000-00002 · Cited by 114
Handbook of Electroencephalography and Clinical Neurophysiology
Roth, B. (1977) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(77)90272-3 · Cited by 88
Cited by
Deep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751
Signal-space projection method for separating MEG or EEG into components
Uusitalo, M. A., & Ilmoniemi, R. J. (1997) · Medical & Biological Engineering & Computing · doi:10.1007/bf02534144 · Cited by 938
Designing optimal spatial filters for single-trial EEG classification in a movement task
Müller-Gerking, J., Pfurtscheller, G., & Flyvbjerg, H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(98)00038-8 · Cited by 846
Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach
He, H., & Wu, D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2913914 · Cited by 569
“In the Euclidean space, after EA, we used CSP [11], [17], [22], [26] for spatial filtering and LDA for classification.”
BackgroundMethodFakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
Ciftci, U. A., Demir, I., & Yin, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3009287 · Cited by 499
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
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- Koles, Z. J., Lazar, M. S., & Zhou, S. Z. (1990). Spatial patterns underlying population differences in the background EEG. Brain Topography. https://doi.org/10.1007/bf01129656
