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.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
De Vries S, Mulder T. Motor imagery and stroke rehabilitation: a critical discussion. J Rehabil Med. 2007;39(1):5–13.
Page SJ, et al. Cortical plasticity following motor skill learning during mental practice in stroke. Neurorehabil Neural Repair. 2009;23(4):382–8.
Marzbani H, Marateb HR, Mansourian M. Neurofeedback: a comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci. 2016;7(2):143.
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.
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.
Pichiorri F, et al. Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness. J Neural Eng. 2011;8(2):025020.
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.
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.
Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett. 1997;239(2):65–8.
Pfurtscheller G, et al. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. 1997;103(6):642–51.
Pfurtscheller G, et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage. 2006;31(1):153–9.
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.
Wolpaw JR, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–91.
Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol. 2008;7(11):1032–43.
Krauledat M, et al. Towards zero training for brain-computer interfacing. PLoS ONE. 2008;3(8):e2967.
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.
Tangermann M, et al. Review of the BCI competition IV. Front Neurosci. 2012;6:55.
Allison BZ, Neuper C. Could anyone use a BCI? In: Brain-computer interfaces. New York: Springer; 2010. p. 35–54.
Ahn M, Jun SC. Performance variation in motor imagery brain-computer interface: a brief review. J Neurosci Methods. 2015;243:103–10.
Friedrich EVC, et al. Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity. Front Hum Neurosci. 2014;8:348.
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.
Hamedi M, Salleh S-H, Noor AM. Electroencephalographic motor imagery brain connectivity analysis for BCI: a review. Neural Comput. 2016;28(6):999–1041.
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.
Gibson RM, et al. Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography. Clin Neurophysiol. 2014;125(8):1556–67.
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.
Lecuyer A, et al. Brain-computer interfaces, virtual reality, and videogames. Computer. 2008;41(10):66–+.
Pfurtscheller G, et al. Walking from thought. Brain Res. 2006;1071(1):145–52.
Kober SE, et al. Learning to modulate one’s own brain activity: the effect of spontaneous mental strategies. Front Hum Neurosci. 2013;7:695.
Franz EA, Zelaznik HN, McCabe G. Spatial topological constraints in a bimanual task. Acta Physiol (Oxf). 1991;77(2):137–51.
Franz EA. Spatial coupling in the coordination of complex actions. The Quarterly Journal of Experimental Psychology: Section A. 1997;50(3):684–704.
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.
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.
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.
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.
Franz EA, et al. Dissociation of spatial and temporal coupling in the bimanual movements of callosotomy patients. Psychol Sci. 1996;7(5):306–10.
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.
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.
Kinsbourne M. Hemispheric specialization and the growth of human understanding. Am Psychol. 1982;37(4):411.
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.
Begliomini C, et al. Exploring manual asymmetries during grasping: a dynamic causal modeling approach. Front Psychol. 2015;6:167.
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.
Volkmann J, et al. Handedness and asymmetry of hand representation in human motor cortex. J Neurophysiol. 1998;79(4):2149–54.
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.
Gerardin E, et al. Partially overlapping neural networks for real and imagined hand movements. Cereb Cortex. 2000;10(11):1093–104.
Dahm SF, Rieger M. Cognitive constraints on motor imagery. Psychol Res. 2016;80(2):235–47.
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.
Kothe C, Makeig S. BCILAB: a platform for brain–computer interface development. J Neural Eng. 2013;10(5):056014.
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.
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.
Blankertz B, et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis. Signal Processing Magazine, IEEE. 2008;25(1):41–56.
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.
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.
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.
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.
Refaeilzadeh P, Tang L, Liu H. Cross-validation. In: Encyclopedia of database systems. New York: Springer; 2009. p. 532–8.
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.
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.
Franz EA, McCormick R. Conceptual unifying constraints override sensorimotor interference during anticipatory control of bimanual actions. Exp Brain Res. 2010;205(2):273–82.
Blankertz B, et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage. 2010;51(4):1303–9.
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.
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.
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.
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.
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.
Conflict of interest:
The authors declare that they have no conflict of interest.
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.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Below is the link to the electronic supplementary material.
About this article
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). https://doi.org/10.1007/s13755-021-00142-y
- Motor imagery
- Machine learning