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Adding Tactile Feedback and Changing ISI to Improve BCI Systems’ Robustness: An Error-Related Potential Study

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Abstract

Nowadays, the brain-computer interface (BCI) systems attract much more attention than before, yet they have not found their ways into our lives since their accuracy is not satisfying. Error Related Potential (ErRP) is a potential that occurs in human brain signals when an unintended event happens, against ones’ will and thoughts. An example is the occurrence of an error in BCI systems. Investigation of the ErRP could enable researchers to increase the accuracy of BCI systems by detecting instances of inaccuracy in the system. In this research the effects of two parameters on the ErRP are studied: (1) The Motor Imagery Time, also known as Inter-Stimulus Interval (ISI) and (2) different types of feedback (Visual and Tactile). The statistical analysis of the ErRP characteristics showed that feedback type meaningfully affects the ErRP in a cue-paced BCI system and it will affect the time of occurrence of this potential. To validate the proposed idea, different feature extraction, and classification techniques were used for the classification of the BCI system responses. It was shown that by proper selection of the parameters and features, the accuracy of the system could be improved. Tactile feedback together with higher ISI could increase the accuracy of finding erroneous trials up to 90%. The proposed method’s accuracy was significantly higher (p-value < 0.05) compared to other methods of feature extraction.

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Data are available on request to the authors.

References

  • Bouton C, Shaikhouni A, Annetta N, Bockbrader M, Friedenberg D, Nielson D, Sharma G, Sederberg P, Glenn B, Mysiw W, Morgan A, Deogaonkar M, Rezai A (2016) Restoring cortical control of functional movement in a human with quadriplegia. Nature 533:247–250. https://doi.org/10.1038/nature17435

    Article  CAS  PubMed  Google Scholar 

  • Chavarriaga R, Perrin X, Siegwart R, del Millán JR (2012) Anticipation-and error-related EEG signals during realistic human-machine interaction: a study on visual and tactile feedback. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 6723–6726. https://doi.org/10.1109/EMBC.2012.6347537

  • Edelman BJ, Meng J, Suma D, Zurn C, Nagarajan E, Baxter BS, Cline CC, He B (2019) Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot 4:aaw6844. https://doi.org/10.1126/scirobotics.aaw6844

    Article  Google Scholar 

  • Ehrlich SK, Cheng G (2019) A feasibility study for validating robot actions using EEG-based error-related potentials. Int J Soc Robot 11:271–283. https://doi.org/10.1007/s12369-018-0501-8

    Article  Google Scholar 

  • Falkenstein M, Hoormann J, Christ S, Hohnsbein J (2000) ERP components on reaction errors and their functional significance: a tutorial. Biol Psychol 51:87–107. https://doi.org/10.1016/S0301-0511(99)00031-9

    Article  CAS  PubMed  Google Scholar 

  • Ferrez PW (2007) EEG-based brain-computer interaction: improved accuracy by automatic single-trial error detection In: Advances in neural information processing systems 21. pp 1–8

  • Ferrez PW, Millán JDR (2005) You are wrong! Automatic detection of interaction errors from brain waves. In: Proceedings of the 19th international joint conference on artificial intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 1413–1418

  • Ferrez PW, del Millán JR (2007) Error-related EEG potentials in brain-computer interfaces. EPFL, Lausanne

    Google Scholar 

  • Ferrez PW, del Millán JR (2008) EEG-based brain-computer interaction: improved accuracy by automatic single-trial error detection. In: Neural information processing systems conference 441–448

  • He B, Yuan H, Meng J, Gao S (2020) Brain-computer interfaces. In: He B (ed) Neural engineering. Springer, Cham, pp 131–183

    Chapter  Google Scholar 

  • Holroyd CB, Coles MGH (2002) The neural basis of human error processing: reinforcement learning, dopamine and the error-related negativity. Psychol Rev 109(4):679–709

    Article  Google Scholar 

  • Iwane F, Halvagal MS, Batzianoulis I, Chavarriaga R, del Millán JR (2019) Inferring subjective preferences on robot trajectories using EEG signals. In: IEEE conference proceedings (IEEE Conf Proc). pp 255–258

  • Kalatzis I, Piliouras N, Ventouras E, Papageorgiou CC, Rabavilas AD, Cavouras D (2004) Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals. Comput Methods Programs Biomed 75:11–22

    Article  CAS  Google Scholar 

  • Kim S, Kim D, Kim L (2019) Optimization method of error-related potentials to improve MI-BCI performance. In: 7th International Winter Conference on Brain—Computer Interface, pp 1–5

  • Meng J, Zhang S, Bekyo A, Olsoe J, Baxter B, He B (2016) Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci Rep. https://doi.org/10.1038/srep38565

    Article  PubMed  PubMed Central  Google Scholar 

  • Miltner WHR, Braun CH, Coles MGH (1997) Event-related brain potentials following incorrect feedback in a time-estimation task: evidence for a “generic” neural system for error detection. J Cogn Neurosci 9:788–798. https://doi.org/10.1162/jocn.1997.9.6.788

    Article  CAS  PubMed  Google Scholar 

  • Pinto A, Nardari G, Mijam M, Morya E, Romero R (2019) A serious game to build a database for ErrP signal recognition. In: International work—conference on artificial neural networks. pp 186–197

  • Soekadar S, Witkowski M, Gómez C, Opisso E, Medina J, Cortese M, Cempini M, Carrozza MC, Cohen LG, Birbaumer N, Vitiello N (2016) Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia. Sci Robot 1(1):3296

    Article  Google Scholar 

  • Spuler M, Niethammer C (2015) Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity. Front Hum Neurosci 9:1–10. https://doi.org/10.3389/fnhum.2015.00155

    Article  Google Scholar 

  • Tessadori J, Schiatti L, Barresi G, Mattos LS (2017) Does tactile feedback enhance single-trial detection of error-related EEG potentials ? In: 2017 IEEE international conference on systems, man, and cybernetics (SMC). pp 1417–1422

  • Usama N, Kunz Leerskov K, Niazi IK, Dremstrup K, Jochumsen M (2020) Classification of error-related potentials from single-trial EEG in association with executed and imagined movements: a feature and classifier investigation. Med Biol Eng Comput 58:2699–2710. https://doi.org/10.1007/s11517-020-02253-2

    Article  PubMed  Google Scholar 

  • van Schie HT, Rogier BM, Michael GHC, Harold B (2004) Modulation of activity in medial frontal and motor cortices during error observation. Nat Neurosci 7:549–554

    Article  Google Scholar 

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Correspondence to Farnaz Ghassemi.

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Handling Editor: Bin He.

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Ahkami, B., Ghassemi, F. Adding Tactile Feedback and Changing ISI to Improve BCI Systems’ Robustness: An Error-Related Potential Study. Brain Topogr 34, 467–477 (2021). https://doi.org/10.1007/s10548-021-00840-6

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