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The common spatial pattern (CSP) approach is known as one of the most popular spatial filtering techniques for EEG classification in motor imagery (MI) based brain-computer interfaces (BCIs).
Eigenvalue and generalized eigenvalue problems: Tutorial
Ghojogh, B., Karray, F., & Crowley, M. (2019) · 1903
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Introduction to Statistical Pattern Recognition
Fukunaga, K. (1990) · 1990
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Independent component approach to the analysis of EEG and MEG recordings
Vigario, R., Sarela, J., Jousmiki, V., Hamalainen, M., & Oja, E. (2000) · 2000
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Solving the small sample size problem of LDA
Huang, R., Liu, Q., Lu, H., & Ma, S. (2002) · 2002
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The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials
Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T., Schalk, G., Wolpaw, J., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., Schroder, M., & Birbaumer, N. (2004) · 2004
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Spatio-Spectral Filters for Improving the Classification of Single Trial EEG
Lemm, S., Blankertz, B., Curio, G., & Muller, K.-R. (2005) · 2005
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Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks
Pfurtscheller, G., Brunner, C., Schlögl, A., & Lopes da Silva, F. (2006) · 2005
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Pattern Recognition and Machine Learning
Bishop, C. M. (2006) · 2006
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The BCI competition III: Validating alternative approaches to actual BCI problems
Blankertz, B., Muller, K.-R., Krusienski, D., Schalk, G., Wolpaw, J., Schlogl, A., Pfurtscheller, G., Millan, J., Schroder, M., & Birbaumer, N. (2006) · 2006
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Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing
Dornhege, G., Blankertz, B., Krauledat, M., Losch, F., Curio, G., & Muller, K.-R. (2006) · 2006
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The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects
Blankertz, B., Dornhege, G., Krauledat, M., Müller, K.-R., & Curio, G. (2007) · 2007
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Toward Brain-Computer Interfacing
Dornhege, G. (Ed.) (2007) · 2007
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Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes
Popescu, F., Fazli, S., Badower, Y., Blankertz, B., & Müller, K.-R. (2007) · 2007
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Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-r. (2008) · 2008
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Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction
Grosse-Wentrup, M., & Buss, M. (2008) · 2008
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Classifying Single-Trial EEG During Motor Imagery by Iterative Spatio-Spectral Patterns Learning (ISSPL)
Wu, W., Gao, X., Hong, B., & Gao, S. (2008) · 2008
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Nonstationary Brain Source Separation for Multiclass Motor Imagery
Gouy-Pailler, C., Congedo, M., Brunner, C., Jutten, C., & Pfurtscheller, G. (2010) · 2009
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Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms
Lotte, F., & Cuntai Guan (2011) · 2010
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Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces
Chi, Y. M., Wang, Y.-T., Wang, Y., Maier, C., Jung, T.-P., & Cauwenberghs, G. (2012) · 2011
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Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications
Grozea, C., Voinescu, C. D., & Fazli, S. (2011) · 2011
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L1-Norm-Based Common Spatial Patterns
Wang, H., Tang, Q., & Zheng, W. (2012) · 2011
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Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b
Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012) · 2012
Cited alongside, same era.
Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification
Higashi, H., & Tanaka, T. (2013) · 2012
Cited alongside, same era.
Sparse spatial filter via a novel objective function minimization with smooth 𝓁 \mathscr{l} 1 regularization
Onaran, I., Ince, N. F., & Cetin, A. E. (2013) · 2012
Cited alongside, same era.
Stationary common spatial patterns for brain–computer interfacing
Samek, W., Vidaurre, C., Müller, K.-R., & Kawanabe, M. (2012) · 2012
Cited alongside, same era.
Review of the BCI Competition IV
Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., Nolte, G., Pfurtscheller, G., Preissl, H., Schalk, G., Schlögl, A., Vidaurre, C., Waldert, S., & Blankertz, B. (2012) · 2012
Cited alongside, same era.
Linear discriminant analysis: A detailed tutorial
Tharwat, A., Gaber, T., Ibrahim, A., & Hassanien, A. E. (2017) · 2017
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MNE Scan: Software for real-time processing of electrophysiological data
Esch, L., Sun, L., Klüber, V., Lew, S., Baumgarten, D., Grant, P. E., Okada, Y., Haueisen, J., Hämäläinen, M. S., & Dinh, C. (2018) · 2018
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Towards correlation-based time window selection method for motor imagery BCIs
Feng, J., Yin, E., Jin, J., Saab, R., Daly, I., Wang, X., Hu, D., & Cichocki, A. (2018) · 2018
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A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · 2018
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Designing Phase-Sensitive Common Spatial Pattern Filter to Improve Brain-Computer Interfacing
Chakraborty, B., Ghosh, L., & Konar, A. (2020) · 2019
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Harmonic Mean of Kullback–Leibler Divergences for Optimizing Multi-Class EEG Spatio-Temporal Filters
Wang, H. (2012) · 2012
Cited alongside, same era.
Brain-Computer Interfaces: Principles and Practice
Wolpaw, J. R., & Wolpaw, E. W. (Eds.) (2012) · 2012
Cited alongside, same era.
Optimizing Spatial Filters by Minimizing Within-Class Dissimilarities in Electroencephalogram-Based Brain–Computer Interface
Arvaneh, M., Cuntai Guan, Kai Keng Ang, & Chai Quek (2013) · 2013
Cited alongside, same era.
Matrix Computations
Golub, G. H., & Van Loan, C. F. (2013) · 2013
Cited alongside, same era.
Divergence-Based Framework for Common Spatial Patterns Algorithms
Samek, W., Kawanabe, M., & Muller, K.-R. (2014) · 2013
Cited alongside, same era.
Importance-weighted covariance estimation for robust common spatial pattern
Balzi, A., Yger, F., & Sugiyama, M. (2015) · 2015
Cited alongside, same era.
BNCI Horizon 2020: Towards a roadmap for the BCI community
Brunner, C., Birbaumer, N., Blankertz, B., Guger, C., Kübler, A., Mattia, D., Millán, J. d. R., Miralles, F., Nijholt, A., Opisso, E., Ramsey, N., Salomon, P., & Müller-Putz, G. R. (2015) · 2015
Cited alongside, same era.
Common spatial patterns combined with phase synchronization information for classification of EEG signals
Li, X., Fan, H., Wang, H., & Wang, L. (2019) · 2019
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EEG Signal Processing in MI-BCI Applications With Improved Covariance Matrix Estimators
Olias, J., Martin-Clemente, R., Sarmiento-Vega, M. A., & Cruces, S. (2019) · 2019
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Dynamic time warping-based transfer learning for improving common spatial patterns in brain–computer interface
Azab, A. M., Ahmadi, H., Mihaylova, L., & Arvaneh, M. (2020) · 2020
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Internal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory
Jin, J., Xiao, R., Daly, I., Miao, Y., Wang, X., & Cichocki, A. (2021) · 2020
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MagCPP: A C++ toolbox for Combining Neurofeedback with Magstim transcranial magnetic stimulators
Oppermann, H., Wichum, F., Haueisen, J., Klemm, M., & Esch, L. (2020) · 2020
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Common Spatial Pattern Reformulated for Regularizations in Brain–Computer Interfaces
Wang, B., Wong, C. M., Kang, Z., Liu, F., Shui, C., Wan, F., & Chen, C. L. P. (2021) · 2020
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A neurophysiological approach to spatial filter selection for adaptive brain–computer interfaces
Bennett, J. D., John, S. E., Grayden, D. B., & Burkitt, A. N. (2021) · 2021
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Single-Trial EEG Classification via Common Spatial Patterns with Mixed Lp- and Lq-Norms
Cai, Q., Gong, W., Deng, Y., & Wang, H. (2021) · 2021
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A high-density 256-channel cap for dry electroencephalography
Fiedler, P., Fonseca, C., Supriyanto, E., Zanow, F., & Haueisen, J. (2021) · 2021
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A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI
Gaur, P., Gupta, H., Chowdhury, A., McCreadie, K., Pachori, R. B., & Wang, H. (2021) · 2021
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Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal
Ghanbar, K. D., Rezaii, T. Y., Farzamnia, A., & Saad, I. (2021) · 2021
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Common Spatial Pattern with L21-Norm
Gu, J., Wei, M., Guo, Y., & Wang, H. (2021) · 2021
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Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification
Miao, Y., Jin, J., Daly, I., Zuo, C., Wang, X., Cichocki, A., & Jung, T.-P. (2021) · 2021
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Improving motor imagery classification during induced motor perturbations
Vidaurre, C., Jorajuría, T., Ramos-Murguialday, A., Müller, K.-R., Gómez, M., & Nikulin, V. V. (2021) · 2021
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Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain–computer interfaces
Zuo, C., Jin, J., Xu, R., Wu, L., Liu, C., Miao, Y., & Wang, X. (2021) · 2021
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How to successfully classify EEG in motor imagery BCI: A metrological analysis of the state of the art
Arpaia, P., Esposito, A., Natalizio, A., & Parvis, M. (2022) · 2022
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