Inter-subject information contributes to the ERP classification in the P300 speller

This study aims to investigate whether the inter-subject information is beneficial to the event-related potential (ERP) classification in the P300-speller. To this end, a classification strategy of weighted ensemble learning generic information (WELGI) was developed, in which the base classifiers constructed by combining both intra- and inter-subject information were integrated into a strong classifier with weight assessments. To verify the algorithm's validity, 55 subjects were recruited to spell 20 characters offline by using the conventional P300-speller paradigm, and the ERP accuracy and precision were investigated. Compared with the traditional classification strategy only using the intra-subject information, the WELGI could achieve significantly higher ERP accuracy and precision. It was demonstrated that the inter-subject information was beneficial to the ERP classification in the P300-speller.

[1]  Jonathan R Wolpaw,et al.  A brain-computer interface for long-term independent home use , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[2]  Yijun Wang,et al.  Visual and Auditory Brain–Computer Interfaces , 2014, IEEE Transactions on Biomedical Engineering.

[3]  Benjamin Schrauwen,et al.  A Bayesian Model for Exploiting Application Constraints to Enable Unsupervised Training of a P300-based BCI , 2012, PloS one.

[4]  Cuntai Guan,et al.  Asynchronous P300-Based Brain--Computer Interfaces: A Computational Approach With Statistical Models , 2008, IEEE Transactions on Biomedical Engineering.

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

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

[7]  Hubert Cecotti,et al.  Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Andrzej Cichocki,et al.  Whether generic model works for rapid ERP-based BCI calibration , 2013, Journal of Neuroscience Methods.

[10]  Wojciech Samek,et al.  Transferring Subspaces Between Subjects in Brain--Computer Interfacing , 2012, IEEE Transactions on Biomedical Engineering.

[11]  Edward R. Dougherty,et al.  Coefficient of determination in nonlinear signal processing , 2000, Signal Process..

[12]  E. W. Sellers,et al.  Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.

[13]  Yuanqing Li,et al.  A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system , 2008, Pattern Recognit. Lett..

[14]  Benjamin Schrauwen,et al.  A Unified Probabilistic Approach to Improve Spelling in an Event-Related Potential-Based Brain–Computer Interface , 2013, IEEE Transactions on Biomedical Engineering.

[15]  J. Wolpaw,et al.  EEG correlates of P300-based brain–computer interface (BCI) performance in people with amyotrophic lateral sclerosis , 2012, Journal of neural engineering.

[16]  Cuntai Guan,et al.  Unsupervised brain computer interface based on inter-subject information , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  D.J. McFarland,et al.  The wadsworth BCI research and development program: at home with BCI , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[19]  Alain Rakotomamonjy,et al.  BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller , 2008, IEEE Transactions on Biomedical Engineering.

[20]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[21]  Xingyu Wang,et al.  Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.