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Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations

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Abstract

To date, most EEG-based brain–computer interface (BCI) studies have focused only on enhancing BCI performance in such areas as classification accuracy and information transfer rate. In practice, however, test–retest reliability of the developed BCI systems must also be considered for use in long-term, daily life applications. One factor that can affect the reliability of BCI systems is the slight displacement of EEG electrode locations that often occurs due to the removal and reattachment of recording electrodes. The aim of this study was to evaluate and compare various feature extraction methods for motor-imagery-based BCI in terms of robustness to slight changes in electrode locations. To this end, EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes with a 1-cm inter-electrode distance. Eight healthy participants underwent 180 trials of left- and right-hand motor imagery tasks. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold cross-validation and linear discriminant analysis, in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, while PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations.

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References

  1. Bayliss JD (2003) Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans Neural Syst Rehabil Eng 11:113–116

    Article  PubMed  Google Scholar 

  2. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kubler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralysed. Nature 398:297–298

    Article  PubMed  CAS  Google Scholar 

  3. Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G (2007) The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37:539–550

    Article  PubMed  Google Scholar 

  4. Boostani R, Graimann B, Moradi MH, Pfurtscheller G (2007) A comparison approach toward finding the best feature and classifier in cue-based BCI. Med Biol Eng Comput 45:403–412

    Article  PubMed  CAS  Google Scholar 

  5. Brunner C, Scherer R, Graimann B, Supp G, Pfurtscheller G (2006) Online control of a brain–computer interface using phase synchronization. IEEE Trans Biomed Eng 53:2501–2506

    Article  PubMed  Google Scholar 

  6. Cecotti H (2011) Spelling with non-invasive brain–computer interfaces—current and future trends. J Physiol-Paris 105:106–114

    Article  PubMed  Google Scholar 

  7. Chatterjee A, Aggarwal V, Ramos A, Acharya S, Thakor NV (2007) A brain–computer interface with vibrotactile biofeedback for haptic information. J NeuroEng Rehabil 4:40

    Article  PubMed  Google Scholar 

  8. Cong W, Bin X, Jie L, Wenlu Y, Dianyun X, Velez AC, Hong Y (2011) Motor imagery BCI-based robot arm system. In: Proceedings of the Seventh International Conference on Natural Computation (ICNC), Shanghai, China, pp 181–184

  9. Coyle S, Ward T, Markham C, McDarby G (2004) On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces. Physiol Meas 25:815–822

    Article  PubMed  Google Scholar 

  10. Guger C, Ramoser H, Pfurtscheller G (2000) Real-time EEG analysis with subject-specific spatial patterns for a brain–computer interface (BCI). IEEE Trans Rehabil Eng 8:447–456

    Article  PubMed  CAS  Google Scholar 

  11. Gysels E, Celka P (2004) Phase synchronization for the recognition of mental tasks in a brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 12:406–415

    Article  PubMed  Google Scholar 

  12. Gysels E, Renevey P, Celka P (2005) SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in brain–computer interfaces. Signal Process 85:2178–2189

    Article  Google Scholar 

  13. Hoffmann U, Vesin JM, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167:115–125

    Article  PubMed  Google Scholar 

  14. Hwang HJ, Kwon K, Im CH (2009) Neurofeedback-based motor imagery training for brain–computer interface (BCI). J Neurosci Methods 179:150–156

    Article  PubMed  Google Scholar 

  15. Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19:153–158

    Article  Google Scholar 

  16. Kamousi B, Liu Z, He B (2005) Classification of motor imagery tasks for brain–computer interface applications by means of two equivalent dipoles analysis. IEEE Trans Neural Syst Rehab Eng 13:166–171

    Article  Google Scholar 

  17. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8:194–208

    Article  PubMed  CAS  Google Scholar 

  18. Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4:R1–R13

    Article  PubMed  CAS  Google Scholar 

  19. McFarland DJ, Wolpaw JR (2008) Brain–computer interface operation of robotic and prosthetic devices. Computer 41:52–56

    Article  Google Scholar 

  20. Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kubler A (2007) An MEG-based brain–computer interface (BCI). Neuroimage 36:581–593

    Article  PubMed  Google Scholar 

  21. Middendorf M, McMillan G, Calhoun G, Jones KS (2000) Brain–computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng 8:211–214

    Article  PubMed  CAS  Google Scholar 

  22. Neuper C, Scherer R, Reiner M, Pfurtscheller G (2005) Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Cogn Brain Res 25:668–677

    Article  Google Scholar 

  23. Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G (2009) Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clin Neurophysiol 120:239–247

    Article  PubMed  Google Scholar 

  24. Neuper C, Schlogl A, Pfurtscheller G (1999) Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. J Clin Neurophysiol 16:373–382

    Article  PubMed  CAS  Google Scholar 

  25. Pfurtscheller G, Brunner C, Schlogl A, da Silva FHL (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31:153–159

    Article  PubMed  CAS  Google Scholar 

  26. Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain–computer communication. Proc IEEE 89:1123–1134

    Article  Google Scholar 

  27. Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M (1997) EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol 103:642–651

    Article  PubMed  CAS  Google Scholar 

  28. Pfurtscheller G, Stancak A, Edlinger G (1997) On the existence of different types of central beta rhythms below 30 Hz. Electroencephalogr Clin Neurophysiol 102:316–325

    Article  PubMed  CAS  Google Scholar 

  29. Rebsamen B, Guan CT, Zhang HH, Wang CC, Teo C, Ang MH, Burdet E (2010) A brain controlled wheelchair to navigate in familiar environments. IEEE Trans Neural Syst Rehabil 18:590–598

    Article  Google Scholar 

  30. Schalk G, Leuthardt EC (2011) Brain–computer interfaces using electrocorticographic signals. IEEE Rev Biomed Eng 4:140–154

    Article  PubMed  Google Scholar 

  31. Sharma N, Pomeroy VM, Baron JC (2006) Motor imagery: a backdoor to the motor system after stroke? Stroke 37:1941–1952

    Article  PubMed  Google Scholar 

  32. Siuly, Yan L, Jinglong W, Jingjing Y (2011) Developing a logistic regression model with cross-correlation for motor imagery signal recognition. In: Proceedings of the 2011 IEEE/ICME International Conference on Complex Medical Engineering (CME), Harbin, China, pp 502–507

  33. Siuly S, Li Y (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Trans Neural Syst Rehabil 20:526–538

    Article  Google Scholar 

  34. Thomas KP, Guan CT, Lau CT, Vinod AP, Ang KK (2009) A new discriminative common spatial pattern method for motor imagery brain–computer interfaces. IEEE Trans Biomed Eng 56:2730–2733

    Article  PubMed  Google Scholar 

  35. Vidaurre C, Scherer R, Cabeza R, Schlogl A, Pfurtscheller G (2007) Study of discriminant analysis applied to motor imagery bipolar data. Med Biol Eng Comput 45:61–68

    Article  PubMed  Google Scholar 

  36. Weiskopf N, Veit R, Erb M, Mathiak K, Grodd W, Goebel R, Birbaumer N (2003) Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19:577–586

    Article  PubMed  Google Scholar 

  37. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) Brain–computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8:164–173

    Article  PubMed  CAS  Google Scholar 

  38. Wolpaw JR, McFarland DJ, Vaughan TM (2000) Brain–computer interface research at the Wadsworth Center. IEEE Trans Rehabil Eng 8:222–226

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Public Welfare and Safety Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (No. 2011-0027859) and in part by the Original Technology Research Program for Brain Science through a National Research Foundation of Korea (NRF) Grant funded by the Ministry of Education, Science, and Technology (No. 2012-0006331).

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Seoul National University, Seoul, 133-791, Republic of Korea

    Sun-Ae Park, Jong-Ho Choi & Hyun-Kyo Jung

  2. Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 133-791, Republic of Korea

    Han-Jeong Hwang, Jeong-Hwan Lim & Chang-Hwan Im

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  1. Sun-Ae Park
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Correspondence to Chang-Hwan Im.

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S.-A. Park and H.-J. Hwang are co-first authors.

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Park, SA., Hwang, HJ., Lim, JH. et al. Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations. Med Biol Eng Comput 51, 571–579 (2013). https://doi.org/10.1007/s11517-012-1026-1

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