Regularized CSP with Fisher's criterion to improve classification of single-trial ERPs for BCI

A brain-computer interface (BCI) based on the combination of oddball paradigm and face perception has been introduced. Such BCI mainly exploits three event-related potential (ERP) components, namely vertex positive potential (VPP), N170 and P300 instead of only P300. With different temporal and spatial distributions of the three ERP components, a regularized common spatial pattern (CSP) with Fisher's criterion (FC), named FCCSP, is proposed to extract the most discriminative features for single trial classification of ERP components. With linear discriminant analysis (LDA) classifier, the proposed FCCSP spatial filtering method yields an average classification accuracy of 95.4% on seven healthy subjects for single-trial ERP components, which outperforms no spatial filtering, the CSP and the FC.

[1]  Cuntai Guan,et al.  Spatially Regularized Common Spatial Patterns for EEG Classification , 2010, 2010 20th International Conference on Pattern Recognition.

[2]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[3]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[4]  Benjamin Blankertz,et al.  Robustifying EEG data analysis by removing outliers , 2009 .

[5]  A. Cichocki,et al.  A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.

[6]  J. Polich.  Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[7]  D. Jeffreys,et al.  The vertex-positive scalp potential evoked by faces and by objects , 2004, Experimental Brain Research.

[8]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[9]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[10]  Miguel Castelo-Branco,et al.  P300 spatial filtering and coherence-based channel selection , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[11]  Motoaki Kawanabe,et al.  Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing , 2007, NIPS.

[12]  Urbano Nunes,et al.  Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis , 2011, Journal of Neuroscience Methods.

[13]  Stefan Haufe,et al.  Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.

[14]  Roxane J. Itier,et al.  Face, eye and object early processing: What is the face specificity? , 2006, NeuroImage.

[15]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[16]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[17]  Liqing Zhang,et al.  A Novel Oddball Paradigm for Affective BCIs Using Emotional Faces as Stimuli , 2011, ICONIP.

[18]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[19]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[20]  Haiping Lu,et al.  Regularized common spatial patterns with generic learning for EEG signal classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  T.M. Vaughan,et al.  Common Spatio-Temporal Patterns for the P300 Speller , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[22]  Moritz Grosse-Wentrup,et al.  Beamforming in Noninvasive Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.