Classification of fNIRS Signals for Decoding Right- and Left-Arm Movement Execution Using SVM for BCI Applications

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)

Abstract

Brain–computer interface-based systems help people who are incapable of interacting with the external environment using their peripheral nervous system. BCIs allow users to communicate purely based on their mental processes alone. Signals such as fNIRS corresponding to the imagination of various limb movements can be acquired noninvasively from the brain and translated into commands that can control an effector without using the muscles. The present study aims at classifying Right-Arm and Left-Arm movement combination using SVM. The study also aims at analyzing the efficacy of two different features, namely average signal amplitude and the difference between the average signal amplitudes of ΔHbO and ΔHbR on the accuracies obtained. The combination of these two features is also explored. The results of the study indicate that chosen features yield average accuracies between 70 and 76.67% calculated for all the subjects. The difference of mean amplitudes of ΔHbO and ΔHbR is investigated as one of the features for fNIRS-BCI application, and it yields an average accuracy of 70%. It indicates the possibility of using this feature for evaluating the binary BCI system for practical communication use. Two-feature combination improved the average accuracy value from 70 to 76.67%. The results obtained from the study suggest that distinct patterns of hemodynamic response arising out of Right-Arm and Left-Arm movements can be exploited for the development of BCI which are best described by the features used in the present study.

Keywords

Functional near-infrared spectroscopy Brain–computer interface Support vector machine 

References

  1. 1.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791CrossRefGoogle Scholar
  2. 2.
    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.  https://doi.org/10.1088/0967-3334/25/4/003CrossRefGoogle Scholar
  3. 3.
    Jobsis FF (1977) Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198:1264–1267CrossRefGoogle Scholar
  4. 4.
    Sitaram R, Zhang HH, Guan CT, Thulasidas M, Hoshi Y, Ishiawa A et al (2007) Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. Neuroimage 34:1416–1427.  https://doi.org/10.1016/j.neuroimage.2006.11.005NCrossRefGoogle Scholar
  5. 5.
    Naseer N, Hong K-S (2013) Classification of functional near-infrared spectroscopy signals corresponding to the right-and left-wrist motor imagery for development of a brain-computer interface. Neurosci Lett 553:84–89.  https://doi.org/10.1016/j.neulet.2013.08.021CrossRefGoogle Scholar
  6. 6.
    Buccino AP, Keles HO, Omurtag A (2016) Hybrid EEG-fNIRS asynchronous brain-computer interface for multiple motor tasks. PLoS ONE 11(1)Google Scholar
  7. 7.
    Holper L, Wolf M (2011) Single trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study. J Neuroeng Rehabil 8:34.  https://doi.org/10.1186/1743-0003-8-34CrossRefGoogle Scholar
  8. 8.
    Hong KS, Naseer N, Kim YH (2015) Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI. Neurosci Lett 587:87–92CrossRefGoogle Scholar
  9. 9.
    Strait M, Scheutz M (2014) What we can and cannot (yet) do with functional near infrared spectroscopy. Front Neurosci 8Google Scholar
  10. 10.
    Naseer N, Qureshi NK, Noori FM, Hong KS (2016) Analysis of different classification techniques for two-class functional near-infrared spectroscopy-based brain-computer interface. Comput Intell Neurosci, 5480760,  https://doi.org/10.1155/2016/5480760
  11. 11.
    Robinson N, Zaidi AD, Rana M, Prasad VA, Guan C, Birbaumer N et al (2016) Real-time subject-independent pattern classification of overt and covert movements from fNIRS signals. PLoS ONE 11(7):e0159959.  https://doi.org/10.1371/journal.pone.0159959CrossRefGoogle Scholar
  12. 12.
    Hwang HJ, Lim JH, Kim DW, Im CH (2014) Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces. J Biomed Opt 19(7):077005–077005CrossRefGoogle Scholar
  13. 13.
    Coyle SM, Ward TE, Markham CM (2007) Brain-computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng 4:219–226CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringCentre for Medical Electronics, Anna UniversityChennaiIndia

Personalised recommendations