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Cited by 468EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyMuscle activation and electromyography studies

Transfer Learning in Brain-Computer Interfaces Abstract\uFFFDThe performance of brain-computer interfaces (BCIs) improves with the amount of avail

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Jayaram, Vinay, Alamgir, Morteza, Altun, Yasemin, Scholkopf, Bernhard, Grosse-Wentrup, Moritz·2016·IEEE Computational Intelligence Magazine
DOI 10.1109/mci.2015.2501545
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Abstract

The performance of brain-computer interfaces (BCIs) improves with the amount of available training data; the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.

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  • Transfer learning has been successfully used for BCIs.

    “Transfer learning (TL) [23], which utilizes information in source domains to improve the learning performance in a target domain, has also been successfully used for BCIs [12], [35], [36], [38], [39].”

    From Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2020) · cites this paper as [12]

  • Kobler and Scherer pre-trained a Restricted Boltzmann Machine on a publicly available dataset and then adapted it to new observations in sensory motor rhythm based BCI.

    “Kobler and Scherer [15] pre-trained a Restricted Boltzmann Machine on a publicly available dataset and then adapted it to new observations in sensory motor rhythm based BCI.”

    From Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2020) · cites this paper as [15]

    The paper’s Abstract (p. 1) discusses transfer learning in BCIs, and §2.5 (p. 10) describes adaptation using a Gaussian model. Searches for Kobler, Scherer, and Restricted Boltzmann Machines found no passage describing the claimed pre-training and adaptation. (LLM, conf=0.76)

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Research works printed but not linked: 39

  1. [1][1] J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. A. Forneris, 'An EEG-based brain-computer interface for cursor control,' Electroencephalography and Clinical Neurophysiology , vol. 78, no. 3, pp. 252-259, 1991. -
  2. [2][2] J. R. Wolpaw and D. J. McFarland, 'Multichannel EEG-based brain-computer communication,' Electroencephalography and Clinical Neurophysiology , vol. 90, no. 6, pp. 444-449, 1994. -
  3. [3][3] N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. K¨ ubler, J. Perelmouter, E. Taub, and H. Flor, 'A spelling device for the paralysed,' Nature , vol. 398, no. 6725, pp. 297-298, Mar 1999. -
  4. [4][4] H. Ramoser, J. M¨ uller-Gerking, and G. Pfurtscheller, 'Optimal spatial filtering of single trial EEG during imagined hand movement,' IEEE Transactions on Rehabilitation Engineering , vol. 8, no. 4, pp. 441-446, 2000. -
  5. [5][5] B. Blankertz, G. Curio, and K.-R. M¨ uller, 'Classifying single trial EEG: Towards brain computer interfacing,' in Advances in Neural Information Processing Systems 14 , T. Dietterich, S. Becker, and Z. Ghahramani, Eds. MIT Press, 2002, pp. 157-164. -
  6. [6][6] T. Lal, M. Schr¨ oder, T. Hinterberger, J. Weston, M. Bogdan, N. Birbaumer, and B. Sch¨ olkopf, 'Support vector channel selection in BCI,' IEEE Transactions on Biomedical Engineering , vol. 51, no. 6, pp. 1003-1010, 2004. -
  7. [7][7] M. Grosse-Wentrup, K. Gramann, and M. Buss, 'Adaptive spatial filters with predefined region of interest for EEG based brain-computerinterfaces,' in Advances in Neural Information Processing Systems 19 , B. Sch¨ olkopf, J. Platt, and T. Hoffman, Eds. Cambridge, MA: MIT Press, 2007, pp. 537-544. -
  8. [8][8] N. Hill, T. Lal, M. Schr¨ oder, T. Hinterberger, B. Wilhelm, F. Nijboer, U. Mochty, G. Widman, C. Elger, B. Sch¨ olkopf, A. K¨ ubler, and N. Birbaumer, 'Classifying EEG and ECoG signals without subject training for fast BCI implementation: Comparison of nonparalyzed and completely paralyzed subjects,' IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 14, no. 2, pp. 183-186, June 2006. -
  9. [9][9] B. Blankertz, G. Dornhege, M. Krauledat, K. M¨ uller, and G. Curio, 'The non-invasive berlin brain-computer interface: Fast acquisition of effective performance in untrained subjects,' NeuroImage , vol. 37, no. 2, pp. 539-550, 2007. -
  10. [10][10] M. Grosse-Wentrup and M. Buss, 'Multi-class common spatial pattern and information theoretic feature extraction,' IEEE Transactions on Biomedical Engineering , vol. 55, no. 8, pp. 1991-2000, 2008. -
  11. [12][12] H. A. Abbass, J. Tang, R. Amin, M. Ellejmi, and S. Kirby, 'Augmented cognition using real-time EEG-based adaptive strategies for air traffic controljikes,' in Proceedings of the Human Factors and Ergonomics Society Annual Meeting , vol. 58, no. 1. SAGE Publications, 2014, pp. 230-234. -
  12. [13][13] Z. Koles, 'The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG,' Electroencephalography and Clinical Neurophysiology , vol. 79, no. 6, pp. 440-447, 1991. -
  13. [15][15] M. Krauledat, M. Tangermann, B. Blankertz, and K.-R. M¨ uller, 'Towards zero training for brain-computer interfacing,' PLoS One , vol. 3, no. 8, pp. 1-12, 2008. -
  14. [16][16] S. Fazli, F. Popescu, M. Dan´ oczy, B. Blankertz, K.-R. M¨ uller, and C. Grozea, 'Subject-independent mental state classification in single trials,' Neural Networks , vol. 22, no. 9, pp. 1305-1312, 2009. -
  15. [17][17] H. Kang, Y. Nam, and S. Choi, 'Composite common spatial pattern for subject-to-subject transfer,' IEEE Signal Processing Letters , vol. 16, no. 8, pp. 683-686, Aug. 2009. -
  16. [18][18] H. Kang and S. Choi, 'Bayesian common spatial patterns for multi-subject EEG classification,' Neural Networks , vol. 57, pp. 39-50, 2014. -
  17. [19][19] F. Lotte and C. Guan, 'Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms,' IEEE Transactions on Biomedical Engineering , vol. 58, no. 2, pp. 355-362, 2011. -
  18. [21][21] A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, 'BCI signal classification using a riemannian-based kernel,' in 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012) . Michel Verleysen, 2012, pp. 97-102. -
  19. [22][22] M. Congedo and A. Barachant, 'A special form of SPD covariance matrix for interpretation and visualization of data manipulated with riemannian geometry,' in MaxEnt 2014 , vol. 1641. AIP publishing LLC, 2015, p. 495. -
  20. [23][23] H. Morioka, A. Kanemura, J.-i. Hirayama, M. Shikauchi, T. Ogawa, S. Ikeda, M. Kawanabe, and S. Ishii, 'Learning a common dictionary for subject-transfer decoding with resting calibration,' NeuroImage , vol. 111, pp. 167-178, 2015. -
  21. [24][24] P. von B¨ unau, F. C. Meinecke, F. C. Kir´ aly, and K.-R. M¨ uller, 'Finding stationary subspaces in multivariate time series,' Physical Review Letters , vol. 103, no. 21, p. 214101, 2009. -
  22. [25][25] W. Samek, C. Vidaurre, K.-R. Mller, and M. Kawanabe, 'Stationary common spatial patterns for braincomputer interfacing,' Journal of Neural Engineering , vol. 9, no. 2, p. 026013, 2012. -
  23. [26][26] M. Sugiyama, M. Krauledat, and K.-R. M¨ uller, 'Covariate shift adaptation by importance weighted cross validation,' The Journal of Machine Learning Research , vol. 8, pp. 985-1005, 2007. -
  24. [27][27] Y. Li, H. Kambara, Y. Koike, and M. Sugiyama, 'Application of covariate shift adaptation techniques in brain-computer interfaces,' IEEE Transactions on Biomedical Engineering , vol. 57, no. 6, pp. 1318-1324, 2010. -
  25. [28][28] R. Mohammadi, A. Mahloojifar, and D. Coyle, 'Unsupervised short-term covariate shift minimization for self-paced BCI,' in Proc. of 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) , 2013, pp. 101-106. -
  26. [29][29] M. Arvaneh, I. Robertson, and T. E. Ward, 'Subject-to-subject adaptation to reduce calibration time in motor imagery-based brain-computer interface,' in Proc. of 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) , 2014, pp. 6501-6504. -
  27. [30][30] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, 'Boosting for transfer learning,' in Proceedings of the 24th International Conference on Machine learning . ACM, 2007, pp. 193-200. -
  28. [31][31] H. Yang, C. Guan, K. S. G. Chua, C. C. Wang, P. K. Soon, C. K. Y. Tang, and K. K. Ang, 'Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection,' Journal of Neural Engineering , vol. 11, no. 3, 2014. -
  29. [32][32] P.-J. Kindermans, H. Verschore, D. Verstraeten, and B. Schrauwen, 'A P300 BCI for the masses: Prior information enables instant unsupervised spelling,' in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 710-718. -
  30. [33][33] M. Alamgir, M. Grosse-Wentrup, and Y. Altun, 'Multitask learning for brain-computer interfaces,' in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) , 2010, pp. 17-24. -
  31. [35][35] D. McFarland, L. McCane, S. David, and J. Wolpaw, 'Spatial filter selection for EEG-based communication,' Electroencephalography and Clinical Neurophysiology , vol. 103, pp. 386-394, 1997. -
  32. [37][37] M. Grosse-Wentrup and B. Sch¨ olkopf, 'A brain-computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex,' Journal of Neural Engineering , vol. 11, no. 5, p. 056015, 2014. -
  33. [38][38] A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, and E. Moulines, 'A blind source separation technique using second-order statistics,' IEEE Transactions on Signal Processing , vol. 45, no. 2, pp. 434-444, Feb. 1997. -
  34. [39][39] J. Onton, M. Westerfield, J. Townsend, and S. Makeig, 'Imaging human EEG dynamics using independent component analysis,' Neuroscience & Biobehavioral Reviews , vol. 30, no. 6, pp. 808-822, 2006. -
  35. [40][40] M. Grosse-Wentrup, S. Harmeling, T. Zander, J. Hill, and B. Sch¨ olkopf, 'How to test the quality of reconstructed sources in independent component analysis (ICA) of EEG/MEG data,' in 2013 International Workshop on Pattern Recognition in Neuroimaging (PRNI) . IEEE, 2013, pp. 102-105. -
  36. [41][41] M. Tangermann, K.-R. M¨ uller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. J. Miller, G. R. M¨ uller-Putz et al. , 'Review of the BCI competition IV,' Frontiers in Neuroscience , vol. 6, pp. 1-31, 2012. -
  37. [42][42] K. Yu, V. Tresp, and A. Schwaighofer, 'Learning gaussian processes from multiple tasks,' in ICML '05: Proceedings of the 22nd International Conference on Machine Learning . New York, NY, USA: ACM, 2005, pp. 1012-1019. -
  38. [43][43] Y. Xue, X. Liao, L. Carin, and B. Krishnapuram, 'Multi-task learning for classification with Dirichlet process priors,' J. Mach. Learn. Res. , vol. 8, pp. 35-63, May 2007. -
  39. [44][44] H. Kang and S. Choi, 'Bayesian common spatial patterns with Dirichlet process priors for multi-subject EEG classification,' in The 2012 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2012, pp. 1-6.

Cited by

Sorted by citations

Authors
Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., & Grosse-Wentrup, M.
Affiliations
  • Max Planck Institute for Intelligent Systems (DE)
  • Universität Hamburg (DE)
ORCID
Jayaram, Vinay
Venue
IEEE Computational Intelligence Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016-02
Volume
11
Issue
1
Pages
20-31
ISSN
1556-603X (ISSN-L)
DOI
10.1109/mci.2015.2501545
OpenAlex
W3124617164
Cited by
468
References
4
Citations by year
2026: 42 · 2025: 40 · 2024: 40 · 2023: 42 · 2022: 61 · 2021: 51 · 2020: 63 · 2019: 53 · 2018: 43 · 2017: 22 · 2016: 11
FWCI
27.36
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Computational Intelligence Magazine (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Keywords
brain-computer interface, transfer learning, motor imagery, amyotrophic lateral sclerosis, cross-subject generalization, training data distribution

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