A combination of transductive and inductive learning for handling non-stationarities in motor imagery classification

A major issue for bringing brain-computer interface (BCI) based on electroencephalogram (EEG) recordings outside of laboratories is the non-stationarities of EEG signals. Varying statistical properties of the signals during inter- or intra-session transfers can lead to deteriorated BCI performances over time. These variations may cause the input data distribution to shift when transitioning from the training phase (calibration session) to the testing/operating phase resulting in a covariate shift. We propose to handle this issue using a novel hybrid learning method based on two classifiers, wherein the first classifier allows including new information in the training dataset, and the second classifier performs an overall classification. The proposed method is motivated by the smoothness assumption, i.e., the points that are closest to each other are more likely to share the same label, and may be added online to enrich the training dataset. The method is evaluated on two real-world datasets corresponding to motor imagery detection (BCI competition 2008 dataset 2A and 2B). The results support the conclusion that an improvement in the classification accuracy over traditional inductive learning and semi-supervised learning methods can be obtained.

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

[2]  Cuntai Guan,et al.  A Covariate Shift Minimization Method to Alleviate Non-Stationarity Effects for an Adaptive Brain-Computer Interface , 2010 .

[3]  Takafumi Kanamori,et al.  Density-Difference Estimation , 2012, Neural Computation.

[4]  Zoubin Ghahramani,et al.  Learning from labeled and unlabeled data with label propagation , 2002 .

[5]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.

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

[7]  Yasuharu Koike,et al.  Application of Covariate Shift Adaptation Techniques in Brain–Computer Interfaces , 2010, IEEE Transactions on Biomedical Engineering.

[8]  Ad Aertsen,et al.  Review of the BCI Competition IV , 2012, Front. Neurosci..

[9]  Cuntai Guan,et al.  EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface , 2013, Neural Computation.

[10]  Heung-Il Suk,et al.  A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Cuntai Guan,et al.  Optimizing Spatial Filters by Minimizing Within-Class Dissimilarities in Electroencephalogram-Based Brain–Computer Interface , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[12]  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.

[13]  Girijesh Prasad,et al.  Bispectrum-based feature extraction technique for devising a practical brain–computer interface , 2011, Journal of neural engineering.

[14]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[15]  Alexander Gammerman,et al.  Learning by Transduction , 1998, UAI.

[16]  Reinhold Scherer,et al.  Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[17]  T. Martin McGinnity,et al.  Quantum Neural Network-Based EEG Filtering for a Brain–Computer Interface , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[19]  Girijesh Prasad,et al.  EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments , 2015, Pattern Recognit..

[20]  Girijesh Prasad,et al.  Adaptive learning with covariate shift-detection for non-stationary environments , 2014, 2014 14th UK Workshop on Computational Intelligence (UKCI).

[21]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[22]  Klaus-Robert Müller,et al.  Covariate Shift Adaptation by Importance Weighted Cross Validation , 2007, J. Mach. Learn. Res..

[23]  Girijesh Prasad,et al.  EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments , 2013, AIAI.

[24]  Girijesh Prasad,et al.  Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface , 2015, Soft Computing.

[25]  Girijesh Prasad,et al.  A Covariate Shift Minimisation Method to Alleviate Non-stationarity Effects for an Adaptive Brain-Computer Interface , 2010, 2010 20th International Conference on Pattern Recognition.

[26]  Rajesh P. N. Rao,et al.  Towards adaptive classification for BCI , 2006, Journal of neural engineering.

[27]  Girijesh Prasad,et al.  Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[28]  Girijesh Prasad,et al.  Dataset Shift Detection in Non-stationary Environments Using EWMA Charts , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[29]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b , 2012, Front. Neurosci..

[30]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[31]  Vicenç Gómez,et al.  Adaptive Multiclass Classification for Brain Computer Interfaces , 2014, Neural Computation.

[32]  Sahibsingh A. Dudani.  The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[33]  T. Martin McGinnity,et al.  Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain–Computer Interface , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[35]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[36]  Kouhyar Tavakolian,et al.  Different classification techniques considering brain computer interface applications. , 2006, Journal of neural engineering.

[37]  J. Wolpaw.  Brain–Computer Interface Research Comes of Age: Traditional Assumptions Meet Emerging Realities , 2010, Journal of motor behavior.

[38]  Huosheng Hu,et al.  Adaptive schemes applied to online SVM for BCI data classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  G. Pfurtscheller,et al.  Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  Reinhold Scherer,et al.  A fully on-line adaptive BCI , 2006, IEEE Transactions on Biomedical Engineering.

[41]  Girijesh Prasad,et al.  Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[42]  Hubert Cecotti,et al.  Greedy multi-class label propagation , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[43]  Bernhard Schölkopf,et al.  A Discussion of Semi-Supervised Learning and Transduction , 2006, Semi-Supervised Learning.