Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs


Autoři: Mohammad Hadi Mehdizavareh aff001;  Sobhan Hemati aff001;  Hamid Soltanian-Zadeh aff001
Působiště autorů: CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran aff001;  Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States of America aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226048

Souhrn

Recently, brain-computer interface (BCI) systems developed based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high information transfer rate (ITR) and increasing number of targets. However, SSVEP-based methods can be improved in terms of their accuracy and target detection time. We propose a new method based on canonical correlation analysis (CCA) to integrate subject-specific models and subject-independent information and enhance BCI performance. We propose to use training data of other subjects to optimize hyperparameters for CCA-based model of a specific subject. An ensemble version of the proposed method is also developed for a fair comparison with ensemble task-related component analysis (TRCA). The proposed method is compared with TRCA and extended CCA methods. A publicly available, 35-subject SSVEP benchmark dataset is used for comparison studies and performance is quantified by classification accuracy and ITR. The ITR of the proposed method is higher than those of TRCA and extended CCA. The proposed method outperforms extended CCA in all conditions and TRCA for time windows greater than 0.3 s. The proposed method also outperforms TRCA when there are limited training blocks and electrodes. This study illustrates that adding subject-independent information to subject-specific models can improve performance of SSVEP-based BCIs.

Klíčová slova:

Algorithms – Bandpass filters – Electrode potentials – Electroencephalography – Ensemble methods – Man-computer interface – Optimization – Signal filtering


Zdroje

1. Choi I, Rhiu I, Lee Y, Yun MH and Nam CS. A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives. PLoS One. 2017; 12(4): e0176674. doi: 10.1371/journal.pone.0176674 28453547

2. Nicolas-Alonso L F and Gomez-Gil J. Brain computer interfaces, a review. Sensors. 2012; 12: 1211–79. doi: 10.3390/s120201211 22438708

3. Nuyujukian P, Sanabria J A, Saab J, Pandarinath C, Jarosiewicz B, Blab C H, et al. Cortical control of a tablet computer by people with paralysis. PLoS One. 2018; 13(11): e0204566. doi: 10.1371/journal.pone.0204566 30462658

4. Gao S, Wang Y, Gao X and Hong B. Visual and auditory brain-computer interfaces. IEEE Trans. Biomed. Eng. 2014; 611435–47.

5. Chen X, Chen Z, Gao S and Gao X. A high-ITR SSVEP based BCI speller. Brain-Comput. Interfaces. 2014; 1: 181–91.

6. Spüler M. A high-speed brain-computer interface (BCI) using dry EEG electrodes. PLoS One. 2017; 12(2): e 0172400.

7. Nakanishi M, Wang Y, Wang Y T, Mitsukura Y and Jung T P. A high-speed brain speller using steady-state visual evoked potentials. Int. J. Neural Syst. 2014; 24: 1–18.

8. Zhu D, Bieger J, Molina G G and Aarts R M. A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. 2010; 1: 702357.

9. Vialatte F-B, Maurice M, Dauwels J and Cichocki A. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog. Neurobiol. 2010; 90: 418–38. doi: 10.1016/j.pneurobio.2009.11.005 19963032

10. Jia C, Gao X, Hong B and Gao S. Frequency and phase mixed coding in SSVEP-based brain-computer interface. IEEE Trans. Biomed. Eng. 2011; 58: 200–6. doi: 10.1109/TBME.2010.2068571 20729160

11. Chen X, Wang Y, Nakanishi M, Jung T P and Gao X. Hybrid frequency and phase coding for a high-speed SSVEP-based BCI speller. Proc. 36th Ann. Int. IEEE Conf. Engineering in Medicine and Biology. 2014; Society pp 3993–6.

12. Chen X, Wang Y, Nakanishi M, Gao X, Jung T-P and Gao S. High-speed spelling with a noninvasive brain-computer interface. Proc. Natl Acad. Sci. 2015; 112: E6058–67. doi: 10.1073/pnas.1508080112 26483479

13. Cheng M, Gao X and Gao S. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans. Biomed. Eng. 2002; 49: 1181–6. doi: 10.1109/tbme.2002.803536 12374343

14. Wang Y, Wang R, Gao X and Gao S. A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2006; 14: 234–40. doi: 10.1109/TNSRE.2006.875576 16792302

15. Friman O, Volosyak I and Graser A. Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans. Biomed. Eng. 2007; 54: 742–50. doi: 10.1109/TBME.2006.889160 17405382

16. Lin Z, Zhang C, Wu W and Gao X. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng. 2006; 53: 2610–4. doi: 10.1109/tbme.2006.886577 17152442

17. Zhang Y, Zhou G, Zhao Q, Onishi A, Jin J, Wang X, et al. Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIs. Neural Information Processing (ICONIP 2011) (Lect. Notes Comput. Sci.). 2011; 7062: 287–95.

18. Zhang Y, Zhou G, Jin J, Wang M, Wang X and Cichocki A. L1-regularized multiway canonical correlation analysis for SSVEP-based BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 2013; 21: 887–96. doi: 10.1109/TNSRE.2013.2279680 24122565

19. Zhang Y, Zhou G, Jin J, Wang X and Cichocki A. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. Int. J. Neural Syst. 2014; 24: 1450013. doi: 10.1142/S0129065714500130 24694168

20. Nakanishi M, Wang Y, Chen X, Wang Y-T, Gao X and Jung T-P. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans. Biomed. Eng. 2018; 65: 104–12. doi: 10.1109/TBME.2017.2694818 28436836

21. Tanaka H, Katura T and Sato H. Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data. NeuroImage. 2013; 64: 308–327. doi: 10.1016/j.neuroimage.2012.08.044 22922468

22. Nakanishi M, Wang Y, Wang Y-T and Jung T-P. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. PLoS One. 2015; 10: e0140703. doi: 10.1371/journal.pone.0140703 26479067

23. Zerafa R, Camilleri T, Falzon O and Camilleri K. To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs. J. Neural Eng. 2018; 15: 051001. doi: 10.1088/1741-2552/aaca6e 29869996

24. Yuan P, Chen X, Wang Y, Gao X and Gao S. Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information. J. Neural Eng. 2015; 12: 046006. doi: 10.1088/1741-2560/12/4/046006 26028259

25. Wang Y, Chen X, Gao X and Gao S. A benchmark dataset for SSVEP-based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 2017; 25: 1746–52. doi: 10.1109/TNSRE.2016.2627556 27849543

26. Bin G, Lin Z, Gao X, Hong B and Gao S. The SSVEP topographic scalp maps by canonical correlation analysis. 30th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society. 2008; pp 3759–3762.

27. Bin G, Gao X, Yan Z, Hong B and Gao S. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J. Neural Eng. 2009; 6: 046002. doi: 10.1088/1741-2560/6/4/046002 19494422

28. Russo F D and Spinelli D. Electrophysiological evidence for an early attentional mechanism in visual processing in humans. Vision Res. 1999; 39: 2975–85. doi: 10.1016/s0042-6989(99)00031-0 10664797

29. Chen X, Wang Y, Gao S, Jung T-P and Gao X. Filter bank canonical correlation analysis for implementing a high speed SSVEP-based brain-computer interface. J. Neural Eng. 2015; 12: 46008.

30. Wang Y, Nakanishi M, Wang Y-T and Jung T-P. Enhancing detection of steady-state visual evoked potentials using individual training data. 36th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society. 2014; pp 3037–40.

31. Fukunaga K. Introduction to statistical pattern recognition. San Diego: Academic Press; 1990.

32. Theodoridis S. Introduction to Pattern recognition. Burlington, MA: Academic Press; 2010.

33. Weise T. Global Optimization Algorithms—Theory and Application. 2008. Available from: http://www.it-weise.de/projects/book.pdf.

34. Ang K K, Chin Z Y, Wang C, Guan C and Zhang H. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front. Neurosci. 2012; 6: 1–9. doi: 10.3389/fnins.2012.00001

35. Webster E, Habibzadeh H, Norton J, Vaughan T and Soyata T. An Unsupervised Channel-Selection Method for SSVEP-based BCI Systems. Available from: http://www.tolgasoyata.com/file/webster.uemcon18.pdf. 2018.

36. Nakanishi M, Wang Y and Jung T-P. Session-to-session transfer in detecting steady-state visual evoked potentials with individual training data. Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience. AC 2016 (Lect. Notes Comput. Sci.). 2016; 9743: 253–60.

37. Yang C, Han X, Wang Y, Saab R, Gao S and Gao X. A dynamic window recognition algorithm for SSVEP-based brain-computer interfaces using a spatio-temporal equalizer. Int. J. Neural. Syst. 2018; 28: 1850028. doi: 10.1142/S0129065718500284 30105920

38. Jiang J, Yin E, Wang C, Xu M and Ming D. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J. Neural Eng. 2018; 15: 046025. doi: 10.1088/1741-2552/aac605 29774867

39. Yao Z, Ma X, Wang Y, Zhang X, Liu M, Pei W, et al. High-speed spelling in virtual reality with sequential hybrid BCIs. IEICE Trans. Inf. Syst. 2018; E101.D: 2859–2862.


Článek vyšel v časopise

PLOS One


2020 Číslo 1