Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 28, 2020

The performance of a low-cost bio-amplifier on 3D human arm movement reconstruction

  • Kayode P. Ayodele EMAIL logo , Eniola A. Akinboboye and Morenikeji A. Komolafe

Abstract

Objectives

In this study, the performance of OpenBCI, a low-cost bio-amplifier, is assessed when used for 3D motion reconstruction.

Methods

Eleven scalp electrode locations from three subjects were used, with sampling rate of 125 Hz, subsequently band-pass filtered from 0.5 to 40 Hz. After segmentation into epochs, information-rich frequency ranges were determined using filter bank common spatial filter. Simultaneously, the actual hand motions of subjects were captured using a Microsoft Kinect sensor. Multimodal data streams were synchronized using the lab streaming layer (LSL) application. A modified version of an existing multiple linear regression models was employed to learn the relationship between the electroencephalography (EEG) feature input and the recorded kinematic data. To assess system performance with limited data, 10-fold cross validation was used.

Results

The most information-rich frequency bands for subjects were found to be in the ranges of 5 – 9 Hz and 33 – 37 Hz. Hand lateralization accuracy for the three subjects were 97.4, 78.7 and 96.9% respectively. 3D position reconstructed with an average correlation coefficient of 0.21, 0.47 and 0.38 respectively along three pre-defined axes, with the corresponding average correlation coefficients for velocity being 0.21, 0.36 and 0.25 respectively. The results compare favourably with a cross-section of existing results, while cost-per-electrode costs were 76% lower than the average per-electrode cost for similar systems and 44% lower than the cheapest previously-reported system.

Conclusions

This study has shown that low-cost bio-amplifiers such as the OpenBCI can be used for 3D motion reconstruction tasks.


Corresponding author: Kayode P. Ayodele, Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, E-mail:

Acknowledgments

The workstation used for this research was donated by the Sanofi Family Foundation, Wollongong. The Titan Xp GPU was donated by the NVIDIA Corporation. The authors thank the Akinlolu Foundation, Lagos for its support.

  1. Research funding: Authors state no funding involved.

  2. Conflict of interest: Authors state no conflict of interest.

References

1. Allison BZ, Wolpaw EW, Wolpaw JR. Brain–computer interface systems: progress and prospects. Expert Rev Med Devices 2007;4:463–74. https://doi.org/10.1586/17434440.4.4.463.Search in Google Scholar

2. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 2000;8:164–73. https://doi.org/10.1109/TRE.2000.847807.Search in Google Scholar

3. Van Dokkum LE, Ward T, Laffont I. Brain computer interfaces for neurorehabilitation–its current status as a rehabilitation strategy post-stroke. Ann Phys Rehabil Med 2015;58:3–8. https://doi.org/10.1016/j.rehab.2014.09.016.Search in Google Scholar

4. Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 2008;7:1032–43. https://doi.org/10.1016/S1474-4422(08)70223-0.Search in Google Scholar

5. Purkayastha SS, Jain VK, Sardana HK. Topical review: a review of various techniques used for measuring brain activity in brain computer interfaces. Adv Electron Electr Eng 2014;4:513–22.Search in Google Scholar

6. Hildt E. Brain-computer interaction and medical access to the brain: individual, social and ethical implications. Stud Ethics Law Technol 2010;4. https://doi.org/10.2202/1941-6008.1143.Search in Google Scholar

7. Sleight J, Pillai P, Mohan S. Classification of executed and imagined motor movement EEG signals. Ann Arbor, MI: University of Michigan; 2009.Search in Google Scholar

8. Liao K, Xiao R, Gonzalez J, Ding L. Decoding individual finger movements from one hand using human EEG signals. PLoS One 2014;9:e85192. https://doi.org/10.1371/journal.pone.0085192.Search in Google Scholar PubMed PubMed Central

9. Hammon PS, Makeig S, Poizner H, Todorov E, De Sa VR. Predicting reaching targets from human EEG. IEEE Signal Process Mag 2008;25:69–77. https://doi.org/10.1109/MSP.2008.4408443.Search in Google Scholar

10. Jerbi K, Vidal JR, Mattout J, Maby E, Lecaignard F, Ossandon T, et al. Inferring hand movement kinematics from MEG, EEG and intracranial EEG: from brain-machine interfaces to motor rehabilitation. IRBM 2011;32:8–18. https://doi.org/10.1016/j.irbm.2010.12.004.Search in Google Scholar

11. Bradberry TJ, Gentili RJ, Contreras-Vidal JL. Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. J Neurosci 2010;30:3432–7. https://doi.org/10.1523/JNEUROSCI.6107-09.2010.Search in Google Scholar PubMed PubMed Central

12. Waldert S, Preissl H, Demandt E, Braun C, Birbaumer N, Aertsen A, et al. Hand movement direction decoded from MEG and EEG. J Neurosci 2008;28:1000–8. https://doi.org/10.1523/JNEUROSCI.5171-07.2008.Search in Google Scholar PubMed PubMed Central

13. Jerbi K, Bertrand O, Schoendorff B, Hoffmann D, Minotti L, Kahane P, et al. Online detection of gamma oscillations in ongoing intracerebral recordings: from functional mapping to brain computer interfaces. In: Joint meeting of the 6th international symposium on non-invasive functional source imaging of the brain and heart and the international conference on functional biomedical imaging. IEEE, Hangzhou, China; 2007. 330–3 pp.10.1109/NFSI-ICFBI.2007.4387767Search in Google Scholar

14. Yuan H, Perdoni C, He B. Decoding speed of imagined hand movement from EEG. In: annual international conference of the IEEE engineering in medicine and biology. IEEE, Buenos Aires, Argentina; 2010. 142–5 pp.10.1109/IEMBS.2010.5627414Search in Google Scholar

15. Lv J, Li Y, Gu Z. Decoding hand movement velocity from electroencephalogram signals during a drawing task. Biomed Eng Online 2010;9:64. https://doi.org/10.1186/1475-925X-9-64.Search in Google Scholar PubMed PubMed Central

16. Robinson N, Guan C, Vinod AP. Adaptive estimation of hand movement trajectory in an EEG based brain–computer interface system. J Neural Eng 2015;12:066019. https://doi.org/10.1088/1741-2560/12/6/066019.Search in Google Scholar PubMed

17. Elgendi M, Picon F, Magnenat-Thalmann N, Abbott D. Arm movement speed assessment via a Kinect camera: a preliminary study in healthy subjects. Biomed Eng Online 2014;13:88. https://doi.org/10.1186/1475-925X-13-88.Search in Google Scholar PubMed PubMed Central

18. Chin ZY, Ang KK, Wang C, Guan C, Zhang H. Multi-class filter bank common spatial pattern for four-class motor imagery BCI. In: Annual international conference of the IEEE engineering in medicine and biology society. IEEE, Minneapolis, MN, USA; 2009. 571–4 pp.10.1109/IEMBS.2009.5332383Search in Google Scholar PubMed

19. Park GH, Lee YR, Kim HN. Improved filter selection method for filter bank common spatial pattern for EEG-based BCI systems. Int J Electron Electr Eng 2014;2:101–5. https://doi.org/10.12720/ijeee.2.2.101-105.Search in Google Scholar

20. Ang KK, Chin ZY, Zhang H, Guan C. Filter Bank Common Spatial Pattern (FBCSP) algorithm using online adaptive and semi-supervised learning. In: International joint conference on neural networks. IEEE, San Jose, CA, USA; 2011. 392–6 pp.10.1109/IJCNN.2011.6033248Search in Google Scholar

21. Bentlemsan M, Zemouri ET, Bouchaffra D, Yahya-Zoubir B, Ferroudji K. Random forest and filter bank common spatial patterns for EEG-based motor imagery classification. In: 5th International conference on intelligent systems, modelling and simulation. IEEE, Langkawi, Malaysia; 2014. 235–8 pp.10.1109/ISMS.2014.46Search in Google Scholar

22. Blankertz B, Lemm S, Treder M, Haufe S, Müller KR. Single-trial analysis and classification of ERP components—a tutorial. NeuroImage 2011;56:814–25. https://doi.org/10.1016/j.neuroimage.2010.06.048.Search in Google Scholar PubMed

23. Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, Müller KR. The Berlin brain-computer interface: accurate performance from first-session in BCI-naive subjects. IEEE Trans Biomed Eng 2008;55:2452–62. https://doi.org/10.1109/TBME.2008.923152.Search in Google Scholar PubMed

24. Kim JH, Chavarriaga R, Millán JDR, Lee SW. 3D trajectory reconstruction of upper limb based on EEG. In: Proceedings of the Fifth International Brain-Computer Interface Meeting 2013 (No. CONF). Graz University of Technology Publishing House; 2013. https://doi.org/10.3217/978-3-85125-260-6-137.Search in Google Scholar

25. Kim M, Kim BH, Jo S. Quantitative evaluation of a low-cost noninvasive hybrid interface based on EEG and eye movement. IEEE Trans Neural Syst Rehabil Eng 2015;23:159–68. https://doi.org/10.1109/TNSRE.2014.2365834.Search in Google Scholar PubMed

26. Ofner P, Müller-Putz GR. Decoding of velocities and positions of 3D arm movement from EEG. In: Annual international conference of the IEEE engineering in medicine and biology society. IEEE, San Diego, CA, USA; 2012. 6406–9 pp.10.1109/EMBC.2012.6347460Search in Google Scholar PubMed

27. Abdi H. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat 2010;2:97–106. https://doi.org/10.1002/wics.51.Search in Google Scholar

Received: 2019-04-08
Accepted: 2020-01-31
Published Online: 2020-05-28
Published in Print: 2020-10-25

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 29.3.2024 from https://www.degruyter.com/document/doi/10.1515/bmt-2019-0085/html
Scroll to top button