A Maximum Fitting-based TrAdaBoost Method for Detecting Multiple Subjects' P300 Potentials

Individual difference of brain signal leads to the P300-based interface needs a large amount of training data to construct a pattern recognition model for each subject. Lots of training increases the training cost, and causes the subject’ s fatigue. TrAdaBoost is a method of transfer a learned classifier's information to another classifier. In order to solve the problem of overfitting caused by combining too many classifiers, a novel maximum fitting-based TrAdaBoost (M-TAB) is proposed to identify the P300 potential across multiple subjects. The M-TAB first trains a classifier with a small number of a subject's data. Then it uses this classifier to adjust the weights of many other classifiers that are trained by other subjects' data. The method retains a high accuracy of 91.05% even if the training data is reduced to 33.33%. The M-TAB improves the accuracy and the information transfer rate by 10.65% and 2.31 bits • min-1, compared with the traditional training method.

[1]  Huosheng Hu,et al.  Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb , 2008, IEEE Transactions on Biomedical Engineering.

[2]  Feng Duan,et al.  Design and Performance Evaluation of a Simple Semi-Physical Human-Vehicle Collaborative Driving Simulation System , 2019, IEEE Access.

[3]  Gabriela Ochoa,et al.  Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces , 2017, MOD.

[4]  Toshihisa Tanaka,et al.  Multiband tangent space mapping and feature selection for classification of EEG during motor imagery , 2018, Journal of neural engineering.

[5]  Xingyu Wang,et al.  Sparse Bayesian Classification of EEG for Brain–Computer Interface , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Zhi-Hua Zhou,et al.  Making FLDA applicable to face recognition with one sample per person , 2004, Pattern Recognit..

[7]  Xin Pan,et al.  An Intention-Driven Semi-autonomous Intelligent Robotic System for Drinking , 2017, Front. Neurorobot..

[8]  Tao Liu,et al.  N200-speller using motion-onset visual response , 2009, Clinical Neurophysiology.

[9]  N. Ramsey,et al.  Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. , 2016, The New England journal of medicine.

[10]  W. A. Sarnacki,et al.  Brain–computer interface (BCI) operation: optimizing information transfer rates , 2003, Biological Psychology.

[11]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[12]  Addison W. Bohannon,et al.  Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface , 2016, Front. Neurosci..

[13]  Ming Liu,et al.  Integrated Transfer Learning Algorithm Using Multi-source TrAdaBoost for Unbalanced Samples Classification , 2017, 2017 International Conference on Computing Intelligence and Information System (CIIS).

[14]  Feng Duan,et al.  A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface , 2018, Int. J. Neural Syst..

[15]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[16]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[17]  Long Chen,et al.  Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers , 2016, Int. J. Neural Syst..

[18]  Feng Duan,et al.  A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals , 2019, IEEE Access.

[19]  Bernhard Schölkopf,et al.  Transfer Learning in Brain-Computer Interfaces , 2015, IEEE Computational Intelligence Magazine.