Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach

The electroencephalogram (EEG) is the most widely used input for brain computer interfaces (BCIs), and common spatial pattern (CSP) is frequently used to spatially filter it to increase its signal-to-noise ratio. However, CSP is a supervised filter, which needs some subject-specific calibration data to design. This is time-consuming and not user-friendly. A promising approach for shortening or even completely eliminating this calibration session is transfer learning, which leverages relevant data or knowledge from other subjects or tasks. This paper reviews three existing approaches for incorporating transfer learning into CSP, and also proposes a new transfer learning enhanced CSP approach. Experiments on motor imagery classification demonstrate their effectiveness. Particularly, our proposed approach achieves the best performance when the number of target domain calibration samples is small.

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

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

[3]  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.

[4]  Gérard Dray,et al.  Knowledge Transfer for Reducing Calibration Time in Brain-Computer Interfacing , 2014, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.

[5]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[6]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

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

[8]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[9]  Dongrui Wu,et al.  EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Geng Liu,et al.  Algorithm and Data Optimization Techniques for Scaling to Massively Threaded Systems , 2012, Computer.

[11]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  Brent Lance,et al.  Transfer learning and active transfer learning for reducing calibration data in single-trial classification of visually-evoked potentials , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[13]  Scott E. Kerick,et al.  Brain–Computer Interface Technologies in the Coming Decades , 2012, Proceedings of the IEEE.

[14]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[15]  Zhilin Zhang,et al.  Evolving Signal Processing for Brain–Computer Interfaces , 2012, Proceedings of the IEEE.

[16]  Dongrui Wu,et al.  Online and Offline Domain Adaptation for Reducing BCI Calibration Effort , 2017, IEEE Transactions on Human-Machine Systems.

[17]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[18]  Philip S. Yu,et al.  Adaptation Regularization: A General Framework for Transfer Learning , 2014, IEEE Transactions on Knowledge and Data Engineering.

[19]  Christian Jutten,et al.  Common Spatial Pattern revisited by Riemannian geometry , 2010, 2010 IEEE International Workshop on Multimedia Signal Processing.

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

[21]  Tzyy-Ping Jung,et al.  Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI) , 2017, IEEE Transactions on Fuzzy Systems.

[22]  Brent Lance,et al.  Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR) , 2017, IEEE Transactions on Fuzzy Systems.

[23]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[24]  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.

[25]  Brent Lance,et al.  Reducing Offline BCI Calibration Effort Using Weighted Adaptation Regularization with Source Domain Selection , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[26]  Fabien Lotte,et al.  Brain-Computer Interfaces: Beyond Medical Applications , 2012, Computer.

[27]  Brent Lance,et al.  Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.