Transferring Subspaces Between Subjects in Brain--Computer Interfacing

Compensating changes between a subjects' training and testing session in brain-computer interfacing (BCI) is challenging but of great importance for a robust BCI operation. We show that such changes are very similar between subjects, and thus can be reliably estimated using data from other users and utilized to construct an invariant feature space. This novel approach to learning from other subjects aims to reduce the adverse effects of common nonstationarities, but does not transfer discriminative information. This is an important conceptual difference to standard multi-subject methods that, e.g., improve the covariance matrix estimation by shrinking it toward the average of other users or construct a global feature space. These methods do not reduces the shift between training and test data and may produce poor results when subjects have very different signal characteristics. In this paper, we compare our approach to two state-of-the-art multi-subject methods on toy data and two datasets of EEG recordings from subjects performing motor imagery. We show that it can not only achieve a significant increase in performance, but also that the extracted change patterns allow for a neurophysiologically meaningful interpretation.

[1]  Klaus-Robert Müller,et al.  Subject-independent mental state classification in single trials , 2009, Neural Networks.

[2]  Moritz Grosse-Wentrup,et al.  Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI , 2011, Comput. Intell. Neurosci..

[3]  K. Müller,et al.  Finding stationary subspaces in multivariate time series. , 2009, Physical review letters.

[4]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Neil D. Lawrence,et al.  Dataset Shift in Machine Learning , 2009 .

[6]  Boris Reuderink,et al.  Robust Brain-Computer Interfaces , 2011 .

[7]  Motoaki Kawanabe,et al.  Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing , 2007, NIPS.

[8]  Klaus-Robert Müller,et al.  Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.

[9]  Klaus-Robert Müller,et al.  Enhanced Performance by a Hybrid Nirs–eeg Brain Computer Interface , 2022 .

[10]  Klaus-Robert Müller,et al.  Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces , 2011, Neural Computation.

[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]  Moritz Grosse-Wentrup,et al.  Multitask Learning for Brain-Computer Interfaces , 2010, AISTATS.

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

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

[16]  Matthias Krauledat,et al.  Analysis of nonstationarities in EEG signals for improving brain-computer interface performance , 2008 .

[17]  Motoaki Kawanabe,et al.  Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation , 2012, Adaptive computation and machine learning.

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

[19]  Seungjin Choi,et al.  Composite Common Spatial Pattern for Subject-to-Subject Transfer , 2009, IEEE Signal Processing Letters.

[20]  Motoaki Kawanabe,et al.  Stationary common spatial patterns for brain–computer interfacing , 2012, Journal of neural engineering.

[21]  Motoaki Kawanabe,et al.  Brain-computer interfacing in discriminative and stationary subspaces , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Cuntai Guan,et al.  Learning from other subjects helps reducing Brain-Computer Interface calibration time , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[23]  F. Meinecke,et al.  Analysis of Multimodal Neuroimaging Data , 2011, IEEE Reviews in Biomedical Engineering.

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

[25]  Klaus-Robert Müller,et al.  Towards Zero Training for Brain-Computer Interfacing , 2008, PloS one.

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