Higher-order PLS for classification of ERPs with application to BCIs

The EEG signals recorded during Brain Computer Interfaces (BCIs) are naturally represented by multi-way arrays in spatial, temporal, and frequency domains. In order to effectively extract the underlying components from brain activities which correspond to the specific mental state, we propose the higher-order PLS approach to find the latent variables related to the target labels and then make classification based on latent variables. To this end, the low-dimensional latent space can be optimized by using the higher-order SVD on a cross-product tensor, and the latent variables are considered as shared components between observed data and target output. The EEG signals recorded under the P300-type affective BCI paradigm were used to demonstrate the effectiveness of our new approach.

[1]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective , 2014, IEEE Signal Processing Magazine.

[2]  H. Wold.  Soft Modelling by Latent Variables: The Non-Linear Iterative Partial Least Squares (NIPALS) Approach , 1975, Journal of Applied Probability.

[3]  Joos Vandewalle,et al.  A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..

[4]  D. Yao,et al.  An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface , 2011, PloS one.

[5]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

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

[7]  A. Lenhardt,et al.  An Adaptive P300-Based Online Brain–Computer Interface , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  M. Rantalainen,et al.  OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification , 2006 .

[9]  J. R. Wolpaw,et al.  ' s personal copy A novel P 300-based brain – computer interface stimulus presentation paradigm : Moving beyond rows and columns q , 2010 .

[10]  Age K. Smilde,et al.  Multivariate paired data analysis: multilevel PLSDA versus OPLSDA , 2009, Metabolomics.

[11]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[12]  N. Birbaumer,et al.  An auditory oddball (P300) spelling system for brain-computer interfaces. , 2009, Psychophysiology.

[13]  B. Blankertz,et al.  A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue , 2010, PloS one.

[14]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

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

[16]  Klaus-Robert Müller,et al.  Introduction to machine learning for brain imaging , 2011, NeuroImage.

[17]  S. Wold,et al.  Orthogonal projections to latent structures (O‐PLS) , 2002 .

[18]  S M M Martens,et al.  Overlap and refractory effects in a brain–computer interface speller based on the visual P300 event-related potential , 2009, Journal of neural engineering.

[19]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[20]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[21]  M. Tenenhaus,et al.  Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach , 2003, Human Genetics.

[22]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.