Crosstalk disrupts the production of motor imagery brain signals in brain–computer interfaces


Brain–computer interfaces (BCIs) target specific brain activity for neuropsychological rehabilitation, and also allow patients with motor disabilities to control mobility and communication devices. Motor imagery of single-handed actions is used in BCIs but many users cannot control the BCIs effectively, limiting applications in the health systems. Crosstalk is unintended brain activations that interfere with bimanual actions and could also occur during motor imagery. To test if crosstalk impaired BCI user performance, we recorded EEG in 46 participants while they imagined movements in four experimental conditions using motor imagery: left hand (L), right hand (R), tongue (T) and feet (F). Pairwise classification accuracies of the tasks were compared (LR, LF, LT, RF, RT, FT), using common spatio-spectral filters and linear discriminant analysis. We hypothesized that LR classification accuracy would be lower than every other combination that included a hand imagery due to crosstalk. As predicted, classification accuracy for LR (58%) was reliably the lowest. Interestingly, participants who showed poor LR classification also demonstrated at least one good TR, TL, FR or FL classification; and good LR classification was detected in 16% of the participants. For the first time, we showed that crosstalk occurred in motor imagery, and affected BCI performance negatively. Such effects are effector-sensitive regardless of the BCI methods used; and likely not apparent to the user or the BCI developer. This means that tasks choice is crucial when designing BCI. Critically, the effects of crosstalk appear mitigatable. We conclude that understanding crosstalk mitigation is important for improving BCI applicability.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3


  1. 1.

    De Vries S, Mulder T. Motor imagery and stroke rehabilitation: a critical discussion. J Rehabil Med. 2007;39(1):5–13.

    Article  Google Scholar 

  2. 2.

    Page SJ, et al. Cortical plasticity following motor skill learning during mental practice in stroke. Neurorehabil Neural Repair. 2009;23(4):382–8.

    Article  Google Scholar 

  3. 3.

    Marzbani H, Marateb HR, Mansourian M. Neurofeedback: a comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci. 2016;7(2):143.

    Google Scholar 

  4. 4.

    Kübler A, Neumann N. Brain-computer interfaces—the key for the conscious brain locked into a paralyzed body. In: Laureys S, editor. Progress in brain research. Amsterdam: Elsevier; 2005. p. 513–25.

    Google Scholar 

  5. 5.

    Cincotti, F., et al. EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb. in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012. IEEE.

  6. 6.

    Pichiorri F, et al. Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness. J Neural Eng. 2011;8(2):025020.

    Article  Google Scholar 

  7. 7.

    Kaiser V, et al. First steps toward a motor imagery based stroke BCI: new strategy to set up a classifier. Front Neurosci. 2011;5:86.

    Article  Google Scholar 

  8. 8.

    Ang, K.K., et al. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. 2010. IEEE.

  9. 9.

    Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett. 1997;239(2):65–8.

    Article  Google Scholar 

  10. 10.

    Pfurtscheller G, et al. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. 1997;103(6):642–51.

    Article  Google Scholar 

  11. 11.

    Pfurtscheller G, et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage. 2006;31(1):153–9.

    Article  Google Scholar 

  12. 12.

    Pfurtscheller G, Neuper C. Dynamics of sensorimotor oscillations in a motor task. In: Graimann B, Pfurtscheller G, Allison B, editors. Brain-Computer interfaces: revolutionizing human-computer interaction. Berlin: Springer; 2010. p. 47–64.

    Google Scholar 

  13. 13.

    Wolpaw JR, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–91.

    Article  Google Scholar 

  14. 14.

    Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol. 2008;7(11):1032–43.

    Article  Google Scholar 

  15. 15.

    Krauledat M, et al. Towards zero training for brain-computer interfacing. PLoS ONE. 2008;3(8):e2967.

    Article  Google Scholar 

  16. 16.

    Müller K-R, et al. Machine learning for real-time single-trial EEG-analysis: from brain–computer interfacing to mental state monitoring. J Neurosci Methods. 2008;167(1):82–90.

    Article  Google Scholar 

  17. 17.

    Tangermann M, et al. Review of the BCI competition IV. Front Neurosci. 2012;6:55.

    Article  Google Scholar 

  18. 18.

    Allison BZ, Neuper C. Could anyone use a BCI? In: Brain-computer interfaces. New York: Springer; 2010. p. 35–54.

    Google Scholar 

  19. 19.

    Ahn M, Jun SC. Performance variation in motor imagery brain-computer interface: a brief review. J Neurosci Methods. 2015;243:103–10.

    Article  Google Scholar 

  20. 20.

    Friedrich EVC, et al. Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity. Front Hum Neurosci. 2014;8:348.

    Article  Google Scholar 

  21. 21.

    Lotte F, et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng. 2018;15(3):031005.

    Article  Google Scholar 

  22. 22.

    Hamedi M, Salleh S-H, Noor AM. Electroencephalographic motor imagery brain connectivity analysis for BCI: a review. Neural Comput. 2016;28(6):999–1041.

    MathSciNet  MATH  Article  Google Scholar 

  23. 23.

    Lotte F, Larrue F, Muhl C. Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design. Front Hum Neurosci. 2013;7:568.

    Article  Google Scholar 

  24. 24.

    Gibson RM, et al. Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography. Clin Neurophysiol. 2014;125(8):1556–67.

    Article  Google Scholar 

  25. 25.

    Neuper C, et al. Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin Neurophysiol. 2009;120(2):239–47.

    Article  Google Scholar 

  26. 26.

    Lecuyer A, et al. Brain-computer interfaces, virtual reality, and videogames. Computer. 2008;41(10):66–+.

    Article  Google Scholar 

  27. 27.

    Pfurtscheller G, et al. Walking from thought. Brain Res. 2006;1071(1):145–52.

    Article  Google Scholar 

  28. 28.

    Kober SE, et al. Learning to modulate one’s own brain activity: the effect of spontaneous mental strategies. Front Hum Neurosci. 2013;7:695.

    MathSciNet  Article  Google Scholar 

  29. 29.

    Franz EA, Zelaznik HN, McCabe G. Spatial topological constraints in a bimanual task. Acta Physiol (Oxf). 1991;77(2):137–51.

    Google Scholar 

  30. 30.

    Franz EA. Spatial coupling in the coordination of complex actions. The Quarterly Journal of Experimental Psychology: Section A. 1997;50(3):684–704.

    Article  Google Scholar 

  31. 31.

    Franz, E.A., Bimanual action representation: a window to human evolution. Taking action: Cognitive neuroscience perspectives on the problem of intentional acts, ed. S. Johnston-Frey, 2003: p. 259–88.

  32. 32.

    Nair DG, et al. Cortical and cerebellar activity of the human brain during imagined and executed unimanual and bimanual action sequences: a functional MRI study. Cognitive brain research. 2003;15(3):250–60.

    Article  Google Scholar 

  33. 33.

    Jäncke L, et al. Differential magnetic resonance signal change in human sensorimotor cortex to finger movements of different rate of the dominant and subdominant hand. Cognitive Brain Research. 1998;6(4):279–84.

    Article  Google Scholar 

  34. 34.

    Grefkes C, et al. Dynamic intra- and interhemispheric interactions during unilateral and bilateral hand movements assessed with fMRI and DCM. Neuroimage. 2008;41(4):1382–94.

    Article  Google Scholar 

  35. 35.

    Franz EA, et al. Dissociation of spatial and temporal coupling in the bimanual movements of callosotomy patients. Psychol Sci. 1996;7(5):306–10.

    Article  Google Scholar 

  36. 36.

    Franz EA, Waldie KE, Smith MJ. The effect of callosotomy on novel versus familiar bimanual actions: a neural dissociation between controlled and automatic processes? Psychol Sci. 2000;11(1):82–5.

    Article  Google Scholar 

  37. 37.

    Franz EA. The allocation of attention to learning of goal-directed actions: a cognitive neuroscience framework focusing on the basal ganglia. Front Psychol. 2012;3:535.

    Article  Google Scholar 

  38. 38.

    Kinsbourne M. Hemispheric specialization and the growth of human understanding. Am Psychol. 1982;37(4):411.

    Article  Google Scholar 

  39. 39.

    Bai O, et al. Asymmetric spatiotemporal patterns of event-related desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study. Clin Neurophysiol. 2005;116(5):1213–21.

    Article  Google Scholar 

  40. 40.

    Begliomini C, et al. Exploring manual asymmetries during grasping: a dynamic causal modeling approach. Front Psychol. 2015;6:167.

    Article  Google Scholar 

  41. 41.

    Serrien DJ, Ivry RB, Swinnen SP. Dynamics of hemispheric specialization and integration in the context of motor control. Nat Rev Neurosci. 2006;7(2):160–6.

    Article  Google Scholar 

  42. 42.

    Volkmann J, et al. Handedness and asymmetry of hand representation in human motor cortex. J Neurophysiol. 1998;79(4):2149–54.

    Article  Google Scholar 

  43. 43.

    Lotze M, et al. Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study. J Cogn Neurosci. 1999;11(5):491–501.

    Article  Google Scholar 

  44. 44.

    Gerardin E, et al. Partially overlapping neural networks for real and imagined hand movements. Cereb Cortex. 2000;10(11):1093–104.

    Article  Google Scholar 

  45. 45.

    Dahm SF, Rieger M. Cognitive constraints on motor imagery. Psychol Res. 2016;80(2):235–47.

    Article  Google Scholar 

  46. 46.

    Renard Y, et al. OpenViBE: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence Teleoper Virtual Environ. 2010;19(1):35–53.

    MathSciNet  Article  Google Scholar 

  47. 47.

    Kothe C, Makeig S. BCILAB: a platform for brain–computer interface development. J Neural Eng. 2013;10(5):056014.

    Article  Google Scholar 

  48. 48.

    Neuper C, et al. Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Cogn Brain Res. 2005;25(3):668–77.

    Article  Google Scholar 

  49. 49.

    Chang, C.-Y., et al. Evaluation of artifact subspace reconstruction for automatic EEG artifact removal. in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2018. IEEE.

  50. 50.

    Blankertz B, et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis. Signal Processing Magazine, IEEE. 2008;25(1):41–56.

    Article  Google Scholar 

  51. 51.

    Blankertz B, et al. The BCI competition III: Validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):153–9.

    Article  Google Scholar 

  52. 52.

    Blankertz B, et al. The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage. 2007;37(2):539–50.

    Article  Google Scholar 

  53. 53.

    Guger C, Ramoser H, Pfurtscheller G. Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). IEEE Trans Rehabil Eng. 2000;8(4):447–56.

    Article  Google Scholar 

  54. 54.

    Vidaurre, C., et al. Unsupervised adaptation of the LDA classifier for brain–computer interfaces. in Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course. 2008. Citeseer.

  55. 55.

    Refaeilzadeh P, Tang L, Liu H. Cross-validation. In: Encyclopedia of database systems. New York: Springer; 2009. p. 532–8.

    Google Scholar 

  56. 56.

    Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21.

    Article  Google Scholar 

  57. 57.

    Morash V, et al. Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries. Clin Neurophysiol. 2008;119(11):2570–8.

    Article  Google Scholar 

  58. 58.

    Franz EA, McCormick R. Conceptual unifying constraints override sensorimotor interference during anticipatory control of bimanual actions. Exp Brain Res. 2010;205(2):273–82.

    Article  Google Scholar 

  59. 59.

    Blankertz B, et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage. 2010;51(4):1303–9.

    Article  Google Scholar 

  60. 60.

    Sadiq MT, et al. Motor imagery BCI classification based on novel two-dimensional modelling in empirical wavelet transform. Electron Lett. 2020;56(25):1367–9.

    Article  Google Scholar 

  61. 61.

    Sadiq MT, et al. Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform. IEEE Access. 2019;7:127678–92.

    Article  Google Scholar 

  62. 62.

    Sadiq MT, et al. Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain-computer interfaces. IEEE Access. 2019;7:171431–51.

    Article  Google Scholar 

  63. 63.

    Sadiq MT, Yu X, Yuan Z. Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces. Expert Syst Appl. 2021;164:114031.

    Article  Google Scholar 

Download references


Dr Phoebe S.-H. Neo was supported by EEGSmart during the data collection phase of this study. She was supported by the Department of Psychology and the Department of Computer Science, University of Otago, during the preparation of the manuscript.

Author information



Corresponding author

Correspondence to Phoebe S.-H. Neo.

Ethics declarations

Conflict of interest:

The authors declare that they have no conflict of interest.

Ethical approval:

The procedures of this study were carried out in accordance with the University of Otago Human Ethics Committee.

Consent to participate:

All the participants gave their consent to participate prior to taking part in the study.

Consent for publication:

All the authors gave their consent to publish the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (DOCX 8297 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Neo, P.SH., Mayne, T., Fu, X. et al. Crosstalk disrupts the production of motor imagery brain signals in brain–computer interfaces. Health Inf Sci Syst 9, 13 (2021).

Download citation


  • Rehabilitation
  • Motor imagery
  • BCI
  • EEG
  • Machine learning
  • Crosstalk