Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction

By Moritz Grosse-Wentrup, Martin Buss. IEEE Transactions on Biomedical Engineering, 2008

390 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7c7cb1e8c44ae1a56ff83e9ec9d8ddf4af8410a1

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Citations in the corpus

Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011, background): “It could also be interesting to adapt the presented regularization framework to multiclass CSP approaches based on approximate joint diagonalization such as [30].”
Transfer Learning in Brain-Computer Interfaces (2015, background): “The learned topography is most strongly weighted around the electrodes directly over the motor cortices and the different cortices also have opposite signs, which is in agreement with spatial filters learned in CSP [4], [13] and beamforming [10].”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, methodology): “The extension of the proposed SCSP algorithm for channel selection to multiclass paradigms can be either performed by computing the SCSP using the one-versus-rest approach [33], or by using joint approximate diagonalization [34].”
Introduction to machine learning for brain imaging (2011, methodology): “Originally the CSP algorithm was conceived for discriminating between two classes, but has also been extended to multi-class problems (Dornhege et al., 2004; Grosse-Wentrup and Buss, 2008).”
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b (2012, methodology): “…p ( x j | ) in 1997) given by (cid:19) (cid:20) The classiﬁcation rule of the NBPW classiﬁer is given by single-trial EEG (Ramoser et al., 2000), and several multi-class extensions of the CSP algorithm have been proposed (Dornhege al., 2004a; Dornhege et al., 2004b; Grosse-Wentrup and Buss, 2008).”
Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces (2018, background): “this stage is the Common Spatial Pattern (CSP) [18], [19].”
Brain Computer Interfaces, a Review (2012, background): “Nevertheless, some extensions for multiclass BCIs have also been proposed [184].”
An end-to-end deep learning approach to MI-EEG signal classification for BCIs (2018, methodology): “P The dominating approach for the task of MI-EEG classiﬁcation is the Common Spatial Patterns (CSP) algorithm (Wang et al., 2005; Grosse-Wentrup & Buss, 2008) and variations thereof, like Common Spatio-Spectral Patterns (CSSP) (Aghaei et al., 2016), Filter Bank CSP (FBCSP) (Ang et al., 2008;…”
Multiclass Brain–Computer Interface Classification by Riemannian Geometry (2012, methodology): “The two proposed Riemannian methods are compared with the classical approach consisting in a spatial filtering by multiclass CSP [1], [20] followed by an LDA classification [2] on”
A novel deep learning approach for classification of EEG motor imagery signals (2017, methodology): “CSP has been applied successfully in many MI task recognition studies [7 – 10].”