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ERD analysis method in motor imagery brain–computer interfaces for accurate switch input

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Abstract

Motor imagery brain–computer interface (MIBCI) can control computers using MI. However, input accuracy is low, partly owing to individual variability in event-related desynchronization (ERD) detection among different subjects. In an earlier study, we determined that using a max power in the mu-band method, i.e., the peak trace method (PTM), is effective for ERD detection. In this study, we compare the PTM to the band power method to determine the most effective method for ERD detection during MI tasks. Experimental results indicate that we could detect ERD using the PTM; however, MI-state estimation was difficult. We also found that the PTM might be effective for ERD detection in subjects with MI experience.

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References

  1. Nakamura S, Yoshikawa T, Furuhashi T (2008) A study on Discrimination of Thought on Movement based on ERD (in Japanese). Japan Society for Fuzzy Theory and Intelligent Informatics 24th Fuzzy System Symposium, Osaka, Japan, 2008, p 214–218

  2. Akiyoshi H, Igasaki T, Murayama N (2013) Development of two-directional control BCI using repeated motor imagery (in Japanese). The institute of electronics, information and communication engineers Technical report 112(417):15–20

    Google Scholar 

  3. Wolpaw JR, McFarland DJ (2004), Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. PNAS, Vol. 101, No. 51

  4. Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857

    Article  Google Scholar 

  5. BCI200 Users Tutorial: Introduction to the Mu Rhythm. http://www.bci2000.org/wiki/index.php/User_Tutorial:Introduction_to_the_Mu_Rhythm

  6. Graimann B, Allison B, Pfurtsheller G (2010), Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction (The Frontiers Collection). Springer pp.10-11

  7. Pfurtscheller G, Brunner C, Schlögla A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1):153–159

    Article  Google Scholar 

  8. Müller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B (2008) Machine learning for real-time single-trial EEG-analysis from brain-computer interfacing to mental state monitoring. J Neurosci Methods 167:82–90

    Article  Google Scholar 

  9. Takahashi M, Gouko M, Ito K (2009) Online motor imagery training effect for the appearance of event related desynchrinization (ERD) (in Japanese). Inst Syst Control Inf Eng 22(5):199–205

    Google Scholar 

  10. Nagamori S, Tanaka H (2015) Study of analysis method for improvement of ERD/ERS detection accuracy by signal processing. AHFE2015 A-10

  11. Cannon EN, Yoo KH, Vanderwert RE, Ferrari PE, Woodward AL, Fox NA (2014) Action experience, more than observation, influences mu rhythm desynchronization. PloS One 2014/3/2

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Acknowledgements

This work was supported by Grant-in-Aid for Scientific Research(C) 15K00762.

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Correspondence to Shuhei Nagamori.

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Nagamori, S., Tanaka, H. ERD analysis method in motor imagery brain–computer interfaces for accurate switch input. Artif Life Robotics 22, 83–89 (2017). https://doi.org/10.1007/s10015-016-0336-z

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  • DOI: https://doi.org/10.1007/s10015-016-0336-z

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