Cited by 364EEG and Brain-Computer InterfacesECG Monitoring and AnalysisNeural dynamics and brain function

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

Koles, Zoltan J., Lazar, Michael S., Zhou, Steven Z.·1990·Brain Topography

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1007/bf01129656

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
Quantitative EEG
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
Quantitative EEG
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

Show all 12 references

Items 201–300 of 364

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks
Ma, P., Dong, C., Lin, R., Liu, H., Lei, D., Chen, X., & Liu, H. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1306283 · Cited by 7
Lightweight deep learning models for EEG decoding: a review
Li, Y., Chen, E., Xiao, X., Xu, M., & Ming, D. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae2717 · Cited by 8
A Region of Interest-Based Electrophysiological Source Imaging Technology and its Applications in Analysis of Motor Imagery EEG Signals
Wang, Y., Xu, Z., & Gao, N. (2023) · IEEE Access · doi:10.1109/access.2023.3339857 · Cited by 7
Frequency Domain Channel-Wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces
Huang, X., Choi, K., Liang, S., Zhang, Y., Zhang, Y., Poon, S. K.-S., & Witold Pedrycz, C. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3344295 · Cited by 7
Adaptive Time–Frequency Segment Optimization for Motor Imagery Classification
Huang, J., Li, G., Zhang, Q., Yu, Q., & Li, T. (2024) · Sensors · doi:10.3390/s24051678 · Cited by 7
Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control
Lee, B.-H., Cho, J.-H., Kwon, B.-H., Lee, M., & Lee, S. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3434983 · Cited by 7
To extraction the feature of EEG signals for mental task recognition
Kuremoto, T., Baba, Y., Obayashi, M., Mabu, S., & Kobayashi, K. (2015) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.1109/sice.2015.7285468 · Cited by 7
Sparse common spatial patterns with recursive weight elimination
Goksu, F., Ince, F., & Onaran, İ. (2011) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2011.6189967 · Cited by 7
Designing a Hierarchical Keyboard Layout for Brain Computer Interface Based Text Entry
Hayet, I., Haq, T. F., Mahmud, H., & Hasan, M. K. (2019) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecace.2019.8679487 · Cited by 7
Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain
Li, H., Fan, K., Ma, J., Wang, B., Qiao, X., Yan, Y., Du, W., & Wang, L. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3056911 · Cited by 9
Multi-class MI-EEG Classification: Using FBCSP and Ensemble Learning Based on Majority Voting
Wei, X., Dong, E., & Zhu, L. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9728576 · Cited by 7
Multi-objective metaheuristics for preprocessing EEG data in brain–computer interfaces
Aler, R., Vega, A., Galván, I. M., & Nebro, A. J. (2012) · Engineering Optimization · doi:10.1080/0305215x.2011.641542 · Cited by 7
Using eigenstructure decompositions of time-varying autoregressions in common spatial patterns-based EEG signal classification
Gutiérrez, D., & Salazar-Varas, R. (2012) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2012.03.004 · Cited by 7
Using Empirical Mode Decomposition with Spatio-Temporal dynamics to classify single-trial Motor Imagery in BCI
Davies, S. R. H., & James, C. J. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944656 · Cited by 7
Biologically Inspired Spatial–Temporal Perceiving Strategies for Spiking Neural Network
Zheng, Y., Xue, J., Liu, J., & Zhang, Y. (2025) · Biomimetics · doi:10.3390/biomimetics10010048 · Cited by 6
LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning
Lu, J., Tian, Y., Zhang, Y., Sheng, Q. Z., & Zheng, X. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3699732 · Cited by 8
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder
Ayoobi, N., & Sadeghian, E. B. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871590 · Cited by 6
Automated labeling and online evaluation for self-paced movement detection BCI
Zhang, D., Hansen, C., Frène, F. D., Kærgaard, S. P., Qian, W., & Chen, K. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110383 · Cited by 7
Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs
Ivanov, N., Lio, A., & Chau, T. T. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad17f2 · Cited by 6
A 3D ray traced biological neural network learning model
Yuen, B., Dong, X., & Lű, T. (2024) · Nature Communications · doi:10.1038/s41467-024-48747-7 · Cited by 6
Sensorimotor brain–computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data
Kapralov, N., Idaji, M. J., Stephani, T., Studenova, A. A., Vidaurre, C., Ros, T., Villringer, A., & Nikulin, V. V. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad7a24 · Cited by 6
An Adaptive And Efficient Spatial Filter For Event-Related Potentials
Ghaderi, F., & Straube, S. (2013) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.43579 · Cited by 6
Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps
Samadi, S., Hamid Soltanian-zadeh, A., & Jutten, C. (2016) · Brain Topography · doi:10.1007/s10548-016-0506-2 · Cited by 8
Supervised filters for EEG signal in naturally occurring epilepsy forecasting
Muñoz–Almaraz, F. J., Zamora-Martínez, F., Botella-Rocamora, P., & Pardo, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178808 · Cited by 6
Single-Trial Analysis
Hu, L. (2019) · Springer eBooks · doi:10.1007/978-981-13-9113-2_10 · Cited by 7
Channel and Trials Selection for Reducing Covariate Shift in EEG-based Brain-Computer Interfaces
He, H., & Wu, D. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914414 · Cited by 6
Shared component analysis
Alain De Cheveigné, A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117614 · Cited by 6
RCTP: Regularized common tensor pattern for rapid serial visual presentation spellers
Jalilpour, S., & Sardouie, S. H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102930 · Cited by 6
Visual Imagery-Based Brain-Computer Interaction Paradigms and Neural Encoding and Decoding
Zhao, L., Liu, Y., Gao, J., Ding, P., Wang, F., Gong, A., Nan, W., Fu, Y., & Li, T. (2025) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3548943 · Cited by 5
Abnormal Heart Sound Detection Using Common Spatial Patterns and Random Forests
Alotaiby, T. N., Alsahle, N. A., & Alotibi, G. N. (2025) · Electronics · doi:10.3390/electronics14081512 · Cited by 5
Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks
Guerrero-Méndez, C. D., Blanco-Díaz, C. F., Rivera-Flor, H., Ulhoa, P. H. F., Dias, E. A. F., Andrade, R. M., Delisle-Rodríguez, D., & Bastos-Filho, T. (2024) · NeuroSci · doi:10.3390/neurosci5020012 · Cited by 5
A reliable and reproducible real-time access to sensorimotor rhythm with a small number of optically pumped magnetometers
Fedosov, N., Medvedeva, D., Shevtsov, O., & Ossadtchi, A. E. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/aded35 · Cited by 7
Motor imagery classification using multiresolution analysis and sparse representation of EEG signals
Saidi, P., Atia, G., Paris, A., & Vosoughi, A. (2015) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2015.7418310 · Cited by 5
A common spatial pattern approach for scalp EEG seizure detection
Alotaiby, T. N., El‐Samie, F. E. A., Alshebeili, S. A., Alotaibi, F. M., Aljibrin, K., Alrshod, S. R., Alkhanin, I. M., & Naif, A. (2016) · International Conference on Electronic Devices, Systems and Applications · doi:10.1109/icedsa.2016.7818564 · Cited by 5
NIOBIUM EFFECT ON BASE METAL AND HEAT AFFECTED ZONE MICROSTRUCTURE OF GIRTH WELDED JOINTS
Schino, A. D., & Nunzio, P. E. D. (2017) · Acta Metallurgica Slovaca · doi:10.12776/ams.v23i1.836 · Cited by 5
Common Spatial Patterns Feature Extraction and Support Vector Machine Classification for Motor Imagery with the SecondBrain
Rayatnia, A., & Khanbabaie, R. (2019) · International Journal of Engineering · doi:10.5829/ije.2019.32.09c.08 · Cited by 5
The EEG-based Lower Limb Exoskeleton System Optimization Strategy Based on Channel Selection
Long, X., Ma, Y., Ma, X., Yan, Z., Wang, C., & Wu, X. (2019) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar47638.2019.9044090 · Cited by 5
Exploiting sparse representation in the P300 speller paradigm
Liu, H., Li, Y., & Wang, S. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2019.9010079 · Cited by 5
Common Spatial Pattern EEG decomposition for Phantom Limb Pain detection
Lendaro, E., Balouji, E., Baca, K., Muhammad, A. S., & Ortiz-Catalan, M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630561 · Cited by 5
Translational Algorithms: The Heart of a Brain Computer Interface
Singh, H., & Daly, I. P. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-10978-7_4 · Cited by 5
Prototype algorithm for three-class motor imagery data classification: a step toward development of human–computer interaction-based neuro-aid
Gupta, G. S., Bhatnagar, M., Mohanta, D. K., & Sinha, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820781-9.00001-2 · Cited by 5
Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG
Fan, C.-C., Xie, H., Peng, L., Yang, H., Ni, Z.-L., Wang, G., Zhou, Y.-J., Chen, S., Fang, Z., Huang, S., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560928 · Cited by 5
Detecting Planets from Direct-imaging Observations Using Common Spatial Pattern Filtering
Shapiro, J., Savransky, D., Ruffio, J.-B., Ranganathan, N., & Macintosh, B. (2019) · The Astronomical Journal · doi:10.3847/1538-3881/ab3642 · Cited by 5
Robust Fisher Linear Discriminant Analysis with Generalized Correntropic Loss Function
Qin, X., Wang, S., Chen, B., & Zhang, K. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326644 · Cited by 5
A Multi-Branch Network for Integrating Spatial, Spectral, and Temporal Features in Motor Imagery EEG Classification
Lian, X., Liu, C., Gao, C., Deng, Z., Guan, W., & Gong, Y. (2025) · Brain Sciences · doi:10.3390/brainsci15080877 · Cited by 4
The impacts of temporal autocorrelations on EEG decoding
Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X. H., Cheng, H., & Chen, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108783 · Cited by 13
EEG Emotion Detection Using Multi-Model Classification
Abdullah, M. A., & Christensen, L. R. (2018) · International Conference on Bioinformatics and Systems Biology (BSB) · doi:10.1109/bsb.2018.8770695 · Cited by 4
Spatial spiking neural network for classification of EEG signals for concealed information test
Edla, D. R., Bablani, A., Bhattacharyya, S., Ramesh, D., Cheruku, R., & Boddu, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18698-8 · Cited by 5
Recruiting neural field theory for data augmentation in a motor imagery brain–computer interface
Polyakov, D., Robinson, P. A., Müller, E. J., & Shriki, O. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1362735 · Cited by 4
SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals
Borra, D., Paissan, F., & Ravanelli, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109097 · Cited by 6
Deep optimal transport for domain adaptation on SPD manifolds
Ju, C., & Guan, C. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2025.104347 · Cited by 15
Improving Decoding of the Mental Activities in BCI Systems using Overl apping FBCSP
Rastgoo, H., & Azadi, S. (2016) · Cited by 4
Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods
Castaño-Candamil, S., Meinel, A., & Tangermann, M. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00055 · Cited by 3
Biohybrid Technology for the Detection of Ultralow Concentrations of Trinitrotoluene in Air
Kiroy, V. N., Kosenko, P. O., Shepelev, I. E., Щербань, И. В., Smolikov, A. B., Arsenyev, F. V., Zaborovsky, A. V., Aksenov, V. A., Tivileva, M. I., Gruznov, V. M., et al. (2023) · Journal of Analytical Chemistry · doi:10.1134/s1061934823080099 · Cited by 4
Pattern recognition of schizophrenia based on multidimensional spatial feature fusion
Guo, S., Lin, Y., Zhao, S., Cui, Y., Xia, Y., Chen, K., Yao, D., & Guo, D. (2023) · Brain-Apparatus Communication A Journal of Bacomics · doi:10.1080/27706710.2023.2249036 · Cited by 4
Reduction of Crosstalk in the Electromyogram: Experimental Validation of the Optimal Spatio-Temporal Filter
Raggi, M., Boccia, G., & Mesin, L. (2023) · IEEE Access · doi:10.1109/access.2023.3323209 · Cited by 4
Comparative analysis of neurointerface technologies for the problem of their reasonable choice in human-machine information systems
Butsiy, R., & Lupenko, S. A. (2020) · Scientific journal of the Ternopil national technical university · doi:10.33108/visnyk_tntu2020.04.135 · Cited by 4
Automated identification of neural correlates of continuous variables
Daly, I. P., Hwang, F., Kirke, A., Malik, A., Weaver, J., Williams, D. A. H., Miranda, E. R., & Nasuto, S. J. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.12.012 · Cited by 4
Evaluation of an EEG/Electro-dermal Hybrid Device to Ascertain a User’s Attentional State
Davies, S. R. H., Mistry, U., Millen, L., Skrypchuk, L., & Braithwaite, J. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601006 · Cited by 4
Classification of ECG signals of normal and abnormal subjects using common spatial pattern
Aljafar, L. M., Alotaiby, T. N., Al-Yami, R. R., Alshebeili, S. A., & Zouhair, J. (2016) · International Conference on Electronic Devices, Systems and Applications · doi:10.1109/icedsa.2016.7818547 · Cited by 4
Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG
Kocanaogullari, D., Gall, R., Mak, J., Huang, X., Mullen, K., Ostadabbas, S., Wittenberg, G. F., Grattan, E. S., & Akçakaya, M. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad8efc · Cited by 3
Levels of shared autonomy in brain-robot interfaces: enabling multi-robot multi-human collaboration for activities of daily living
Douglas, H., Vincenzo, M. D., Dossa, R. F. J., Nunziante, L., Sujit, S., & Arulkumaran, K. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1718713 · Cited by 10
Machine-learning applications for differentiation across states/stages of creative thinking based on time-series and time-frequency features of EEG/ERP signals
Shemyakina, N. V., Velikoborets, G. S., & Nagornova, Z. V. (2023) · Genes and Cells · doi:10.23868/gc562731 · Cited by 3
Joint spatial and spectral filter estimation for single trial detection of event related potentials
Ghaderi, F. (2013) · Machine learning for signal processing ... · doi:10.1109/mlsp.2013.6661938 · Cited by 3
A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems
Hovine, C., & Bertrand, A. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096408 · Cited by 3
A periodic spatio-spectral filter for event-related potentials
Ghaderi, F., Kim, S. K., & Kirchner, E. A. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.10.004 · Cited by 3
An Auto-Adaptive Approach Towards Subject-Independent Motor Imagery BCI
Hosseini, S. M., Bavafa, M., & Shalchyan, V. (2019) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme49163.2019.9030390 · Cited by 3
Ontology-Driven Tools for EEG-Based Neurophysiological Research Automation
Ryabinin, K. V., Chuprina, S. I., & Labutin, I. A. (2021) · Scientific Visualization · doi:10.26583/sv.13.4.08 · Cited by 3
Target Frequency Band of Cognition and Tempo of Music: Cardiac Synchronous EEG
Mollakazemi, M. J., Biswal, D., & Patwardhan, A. R. (2018) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2018.8642740 · Cited by 3
IMPROVING THE PERFORMANCE OF MOTOR IMAGERY EEG-BASED BCIS VIA AN ADAPTIVE EPOCH TRIMMING MECHANISM
Kalantar, G., Mirgholami, M., Asif, A. A., & Mohammadi, A. (2018) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2018.8646445 · Cited by 3
Classification of EEG for Upper Limb Motor Imagery: An Approach for Rehabilitation
Paul, Y., & Jaswal, R. A. (2018) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc.2018.8745936 · Cited by 3
Advancing Cross-Subject Domain Generalization in Brain–Computer Interfaces With Multiadversarial Strategies
Liu, Y., Qin, L., Chen, X., Jeannès, R. L. B., Coatrieux, J.-L., & Shu, H. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3566804 · Cited by 2
DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification
Chang, L., Yang, B., Zhang, J., Li, T., Feng, J., & Xu, W. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10296-0 · Cited by 2
Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals
Kauati-Saito, E., Pereira, A. S., Fontana, A. P., de Sá, A. M. F. L. M., Soares, J. G. M., & Tierra-Criollo, C. J. (2025) · Sensors · doi:10.3390/s25175291 · Cited by 3
Beyond Within-Subject Performance: A Multi-Dataset Study of Fine-Tuning in the EEG Domain
Sartzetaki, C., Antoniadis, P., Antonopoulos, N., Gkinis, I., Krasoulis, A., Perdikis, S., & Pitsikalis, V. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394335 · Cited by 3
Riemannian Geometry-Based Spatial Filtering (RSF) Method for Motor Imagery EEG Classification
Pan, L., Wang, K., Sun, X., Xu, M., & Ming, D. (2024) · International Conference on Biomedical Signal and Image Processing (ICBIP) · doi:10.1145/3691521.3691529 · Cited by 2
Temporal and frequency features play different role in recognizing preparation of voluntary movements from electroencephalogram
Sagatdinov, A., Lipkovich, M., Knyazeva, V. M., & Александров, А. Н. (2025) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-025-01513-2 · Cited by 2
Optimized Time Window Segmentation for Motor Imagery EEG Classification: An Ant Colony Optimization-Based Approach
Agustin, R. I., & Raphisak, P. (2025) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci65088.2025.10931335 · Cited by 2
CCLNet: multiclass motor imagery EEG decoding through extended common spatial patterns and CNN-LSTM hybrid network
Singh, K. J., Singha, N., & Bhalaik, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07319-2 · Cited by 3
A Method Based on Filter Bank Common Spatial Pattern for Multiclass Motor Imagery BCI
Xia, Z., Likun, X., & Ma, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33617-2_16 · Cited by 2
Mutual Information-Based Time Window Adaptation for Improving Motor Imagery-Based BCI
Phunruangsakao, C., Achanccaray, D., & Hayashibe, M. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658700 · Cited by 2
Multi-domain feature extraction method of motor imagery EEG signal based on DWT and CSP
Li, N., & Liu, Y. (2022) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1117/12.2631559 · Cited by 2
Analysis of instantaneous brain interactions contribution to a motor imagery classification task
Cuervo, J. H. C., Saa, J. F. D., & Solano, L. A. R. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.990892 · Cited by 2
Neuro-Interfaces Review
Mihaylova, Y., Pozdeeva, A., Leukhin, A., Toschev, A. S., Talanov, M. O., Vallverdú, J., & Suleimanova, A. (2022) · Journal of Artificial Intelligence and Consciousness · doi:10.1142/s270507852230002x · Cited by 2
Correlation Assisted Strong Uncorrelating Transform Complex Common Spatial Patterns for Spatially Distant Channel Data
Kim, Y., You, J., Lee, H. J., Lee, S. M., & Park, C. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/4281230 · Cited by 2
L1-norm unsupervised Fukunaga-Koontz transform
Camargo, J. L., Martı́n-Clemente, R., Hornillo-Mellado, S., & Zarzoso, V. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107942 · Cited by 2
Interpretable riemannian classification in brain-computer interfacing
Xu, J., Grosse‐Wentrup, M., & Jayaram, V. (2020) · TUGraz OPEN Library (Graz University of Technology) · doi:10.3217/978-3-85125-682-6-07 · Cited by 2
Hierarchical emotion-recognition framework based on discriminative brain neural network topology and ensemble co-decision strategyPreprint
Li, C., Li, P., Zhang, Y., Li, N., Si, Y., Li, F., Yao, D., & Xu, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10804 · Cited by 2
Integration of an Eye Gaze Interface and BCI with Biofeedback for Human-Robot Interaction
Sakamaki, I., Tavakoli, M., Wiebe, S. A., & Adams, K. D. (2020) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-n8hm-0p20 · Cited by 2
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal ConversionPreprint
Nørskov, A. V., Zahid, A. N., & Mørup, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.07788 · Cited by 3
Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients
Zuo, C., Yin, Y., Wang, H., Zheng, Z., Ma, X., Yang, Y., Wang, J., Wang, S., Huang, Z., & Ye, C. (2025) · Scientific Data · doi:10.1038/s41597-025-05767-2 · Cited by 2
An Efficient Multiobjective Feature Optimization Approach for Improving Motor Imagery-based Brain-computer Interface Performance
Rezvani, S., & Chaibakhsh, A. (2024) · Caspian Journal of Neurological Sciences · doi:10.32598/cjns.10.36.461.1 · Cited by 1
Detection of the Intention to Perform a Two-Staged Movement from EEG Signals
Sagatdinov, A., Knyazeva, V. M., Lipkovich, M., & Aleksandrov, A. (2024) · International Conference Neurotechnologies and Neurointerfaces (CNN) · doi:10.1109/cnn63506.2024.10705815 · Cited by 1
Common Spatial Pattern Variants for Feature Extraction in Multi-Class Motor Imagery BCI
Asadi, F., Thangthong, K., & Tungjitkusolmun, S. (2025) · Biomedical Engineering International Conference · doi:10.1109/bmeicon66226.2025.11113663 · Cited by 1
OSFTL-PL: Online source-free transfer learning with pseudo-labels for privacy-preserving EEG-based BCI
Liu, S., Chen, K., Zhang, J., Long, J., & Wu, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109326 · Cited by 1
Deep Learning-Based Decoding and Feature Visualization of Motor Imagery Speeds From EEG Signals
Todoroki, S., Phunruangsakao, C., Goto, K., Kutsuzawa, K., Owaki, D., & Hayashibe, M. (2025) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2025.3645617 · Cited by 3
Identifying relevant EEG features for personalized emotional videos: a cross-population analysis in healthy controls and patients with disorders of consciousness
Maza, A., Goizueta, S., Navarro, M. D., Noé, E., Ferri, J., Naranjo, V., & Lloréns, R. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109669 · Cited by 1
Automatic and Semi-automatic Approaches for Selecting Prominent Spatial Filters of CSP in BCI Applications
Suppakun, N., & Maneewongvatana, S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04954-5_30 · Cited by 1
Multiclass electrooculography using common spatial pattern
Duque, C., Ribeiro, M., & Souto, N. M. B. (2012) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp.2012.6256367 · Cited by 1
Phase Variants of the Common Spatial Patterns Method
Camilleri, K. P., Falzon, O., Camilleri, T. A., & Fabri, S. G. (2014) · Neuromethods · doi:10.1007/7657_2013_66 · Cited by 1

Authors: Koles, Z. J., Lazar, M. S., & Zhou, S. Z.
Affiliations: University of Alberta (CA)
Venue: Brain Topography
Publisher: Springer Science and Business Media LLC
Published: 1990
Volume: 2
Issue: 4
Pages: 275-284
ISSN: 0896-0267 (ISSN-L)
1573-6792
DOI: 10.1007/bf01129656
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 ›

Are you an author, or do you see a problem with this page? Request a review

Spatial patterns underlying population differences in the background EEG

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks