Movement Related Potentials Feature Extraction Based on Transfer Learning

and often contains various artifacts. The discriminative spatial pattern (DSP) algo­ rithm successfully improves the signal-to-noise ratio of MRPs. However, abundant labeled training data are required for DSP to learn reliable spatial filters for each subject respectively. This is inconvenient for the applications of BCIs. In this paper, we propose a regularized DSP (RDSP) algorithm for MRP feature extraction , which does not need any labeled training data for a new subject. The regularization function of RDSP is built on empirical maximum mean discrepancy (MMD) to reduce the differences not only in marginal distribution but also in conditional distribution between subjects. RDSP transfers the common discriminative spatial filters across subjects and up­ dates them iteratively by semi-supervised learning. Experiment results on BCI competition datasets show the effectiveness of RDSP.

[1]  Hans-Peter Kriegel,et al.  Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.

[2]  Jun Lu,et al.  Adaptive Spatio-Temporal Filtering for Movement Related Potentials in EEG-Based Brain–Computer Interfaces , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[5]  Haiping Lu,et al.  Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.

[6]  Yong Xiang,et al.  Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Yong Xiang,et al.  Time-Frequency Approach to Underdetermined Blind Source Separation , 2012, IEEE Transactions on Neural Networks and Learning Systems.

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

[9]  Martial Hebert,et al.  Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[10]  Jing Wang,et al.  L1-norm based discriminative spatial pattern for single-trial EEG classification , 2014, Biomed. Signal Process. Control..

[11]  Yuanqing Li,et al.  Surfing the internet with a BCI mouse , 2012, Journal of neural engineering.

[12]  Jun Lv,et al.  Semi-supervised temporal-spatial filter based on MRP for brain-computer interfaces , 2011, 2011 IEEE International Conference on Information and Automation.

[13]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[14]  Haixian Wang,et al.  Local discriminative spatial patterns for movement-related potentials-based EEG classification , 2011, Biomed. Signal Process. Control..

[15]  Philip S. Yu,et al.  Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[16]  Dan Wu,et al.  Combining Spatial Filters for the Classification of Single-Trial EEG in a Finger Movement Task , 2007, IEEE Transactions on Biomedical Engineering.

[17]  G. Pfurtscheller,et al.  An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  John Blitzer,et al.  Co-Training for Domain Adaptation , 2011, NIPS.

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

[20]  W. Freeman,et al.  Topography, independent component analysis and dipole source analysis of movement related potentials , 2007, Cognitive Neurodynamics.

[21]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[22]  Motoaki Kawanabe,et al.  Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces , 2011, IEEE Transactions on Biomedical Engineering.

[23]  Zhaoshui He,et al.  Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering , 2011, IEEE Transactions on Neural Networks.