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Cognitive Computation

, Volume 10, Issue 6, pp 1062–1074 | Cite as

Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach

  • J. Solé-Casals
  • C. F. Caiafa
  • Q. Zhao
  • A. Cichocki
Article

Abstract

One of the current issues in brain-computer interface (BCI) is how to deal with noisy electroencephalography (EEG) measurements organized as multidimensional datasets (tensors). On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements (electrode misconnections, subject movements, etc.) are considered as unknowns (missing samples) that are inferred from a tensor decomposition model (tensor completion). We evaluate the performance of four recently proposed tensor completion algorithms, CP-WOPT (Acar et al. Chemom Intell Lab Syst. 106:41-56, 2011), 3DPB-TC (Caiafa et al. 2013), BCPF (Zhao et al. IEEE Trans Pattern Anal Mach Intell. 37(9):1751-1763, 2015), and HaLRT (Liu et al. IEEE Trans Pattern Anal Mach Intell. 35(1):208-220, 2013), plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI applications. We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery (MI), however, not at the same level as clean data. Summarizing, compared to the interpolation case, all tensor completion algorithms succeed to increase the classification performance by 7–9% (LDA–SVD) for random missing entries and 15–8% (LDA–SVD) for random missing channels. Tensor completion algorithms are useful in real BCI applications. The proposed strategy could allow using motor imagery BCI systems even when EEG data is highly affected by missing channels and/or samples, avoiding the need of new acquisitions in the calibration stage.

Keywords

Brain-computer interface EEG Tensor completion Tensor decomposition Missing samples 

Notes

Funding Information

JSC was supported by the Spanish Ministry of Science and Innovation (Grant No TEC2016-77791-C4-2-R) and the University of Vic – Central University of Catalonia (Grant No R0947). CFC was supported by NSF IIS-1636893, NSF BCS-1734853, NIH NIMH ULTTR001108, and partially supported by the Indiana University Areas of Emergent Research initiative “Learning: Brains, Machines, Children”. QZ was supported by JSPS KAKENHI Grant No. 17K00326, NSFC China Grant No. 61773129 and JST CREST Grant No. JPMJCR1784. AC was partially supported by the MES RF Grant No14.756.31.0001 and the Polish National Science Center Grant No 2016/20/W/N24/00354.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was obtained from all participants.

Human and Animal Rights

All experiments were performed in accordance with the tenets of the Declaration of Helsinki.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Data and Signal Processing Research GroupUniversity of Vic – Central University of CataloniaVicSpain
  2. 2.Instituto Argentino de Radioastronomía (IAR) – CCT-La Plata, CONICET, CICPBABuenos AiresArgentina
  3. 3.Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
  4. 4.Tensor Learning Unit – RIKEN Center for Advanced Intelligence ProjectTokyoJapan
  5. 5.School of AutomationGuangdong University of TechnologyGuangdongChina
  6. 6.Skolkovo Institute of Science and TechnologyMoscowRussia
  7. 7.Department of InformaticsNicolaus Copernicus UniversityTorunPoland
  8. 8.College of Computer ScienceHangzhou Dianzi UniversityHangzhouChina

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