Skip to main content

Investigation of EEG Correlate in NIRS Signal for BCI

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1201)

Abstract

In this study, a unique approach has been presented for performing correlation between the signals of two neuroimaging modalities for motor imagery data. By correlating the signals, we investigate the time sequence relationship between the haemodynamic response and electrophysiological activity during the performance of mental arithmetic vs resting-state and motor imagery activity of right vs left hand, as we correlate near-infrared spectroscopy (NIRS) and electroencephalography (EEG) signals respectively obtained during the same activity. Data of both EEG and NIRS are taken from “Open Access Dataset for EEG+NIRS Single-Trial Classification”. Thirty active electrodes have been used for the EEG data extraction using 1000 Hz as the sampling frequency. International 10-5 system has been employed for electrode placement for EEG signal extraction and one-tailed Pearson’s correlation analysis has been exercised on the responses of prominent channels. By correlating the EEG-NIRS signals, we demonstrate the correlation between haemodynamic response and readiness potential (RP) in the premotor cortex. Both modalities are also used for Mental Workload assessment so our work proves helpful in MWL results extraction too. The suggested correlation method can be utilized for approach validation procedures in future multi-modal BCI research activities.

Keywords

  • Correlation
  • Electrode
  • Electrophysiological
  • Haemodynamic
  • Mental Workload assessment
  • Neuroimaging modalities

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-51041-1_42
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-51041-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Aihara, T., et al.: Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior. Neuroimage 59, 4006–4021 (2012)

    CrossRef  Google Scholar 

  2. Fazli, S., et al.: Enhanced performance by a hybrid NIRS-EEG brain computer interface. Neuroimage 59, 519–529 (2012)

    CrossRef  Google Scholar 

  3. Herrmann, M.J., et al.: Enhancement of activity of the primary visual cortex during processing of emotional stimuli as measured with event related functional near-infrared spectroscopy and event-related potentials. Hum. Brain Mapp. 29, 28–35 (2008)

    CrossRef  Google Scholar 

  4. Näsi, T., et al.: Correlation of visual-evoked hemodynamic responses and potentials in human brain. Exp. Brain Res. 202, 561–570 (2010)

    CrossRef  Google Scholar 

  5. Khan, M.J., Hong, K.S.: Passive BCI based on drowsiness detection: an fNIRS study. Biomed. Optics Express 6(10), 4063–4078 (2015)

    CrossRef  Google Scholar 

  6. Khan, M.J., Hong, K.S.: Hybrid EEG–fNIRS-based eight-command decoding for BCI: application to quadcopter control. Front. Neurorobotics 11, 6 (2017)

    CrossRef  Google Scholar 

  7. Hong, K.S., Khan, M.J.: Hybrid brain–computer interface techniques for improved classification accuracy and increased number of commands: a review. Front. Neurorobotics 11, 35 (2017)

    CrossRef  Google Scholar 

  8. Hong, K.S., Khan, M.J., Hong, M.J.: Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfaces. Front. Hum. Neurosci. 12, 246 (2018)

    CrossRef  Google Scholar 

  9. Khan, M.J., Hong, K.S., Naseer, N., Bhutta, M.R.: A hybrid EEG-fNIRS BCI: motor imagery for EEG and mental arithmetic for fNIRS. In: 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014), pp. 275–278. IEEE, October 2014

    Google Scholar 

  10. Khan, M.J., Hong, M.J., Hong, K.S.: Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface. Front. Hum. Neurosci. 8, 244 (2014)

    Google Scholar 

  11. Wallois, F., Patil, A., Héberlé, C., Grebe, R.: EEG-NIRS in epilepsy in children and neonates. Neurophysiol. Clin. 40, 281–292 (2010)

    CrossRef  Google Scholar 

  12. Biessmann, F., Plis, S., Meinecke, F.C., Eichele, T., Müller, K.-R.: Analysis of multimodal neuroimaging data. IEEE Rev. Biomed. Eng. 4, 26–58 (2011)

    CrossRef  Google Scholar 

  13. Friston, K.J.: Modalities, modes, and models in functional neuroimaging. Science 326, 399–403 (2009)

    CrossRef  Google Scholar 

  14. Shibasaki, H.: Human brain mapping: hemodynamic response and electrophysiology. Clin. Neurophysiol. 119, 731–743 (2008)

    CrossRef  Google Scholar 

  15. Wallois, F., Mahmoudzadeh, M., Patil, A., Grebe, R.: Usefulness of simultaneous EEG-NIRS recording in language studies. Brain Lang. 121, 110–123 (2012)

    CrossRef  Google Scholar 

  16. Pfurtscheller, G., et al.: The hybrid BCI. Front. Neurosci. 4, 30 (2010)

    Google Scholar 

  17. Shibasaki, H., Hallett, M.: What is the Bereitschaftspotential? Clin. Neurophysiol. 117, 2341–2356 (2006)

    CrossRef  Google Scholar 

  18. Jahanshahi, M., et al.: Self-initiated versus externally triggered movements I. An investigation using measurement of regional cerebral blood flow with PET and movement-related potentials in normal and Parkinson’s disease subjects. Brain 118, 913–933 (1995)

    Google Scholar 

  19. Jankelowitz, S.K., Colebatch, J.G.: Movement-related potentials associated with self-paced, cued and imagined arm movements. Exp. Brain Res. 147, 98–107 (2002)

    CrossRef  Google Scholar 

  20. Libet, B., Gleason, C.A., Wright, E.W., Pearl, D.K.: Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). Brain 106, 623–642 (1983)

    CrossRef  Google Scholar 

  21. Toma, K., et al.: Generators of movement-related cortical potentials: fMRI-Constrained EEG dipole source analysis. Neuroimage 17, 161–173 (2002)

    CrossRef  Google Scholar 

  22. Connolly, J.D., Goodale, M.A, Cant, J.S., Munoz, D.P.: Effector-specific fields for motor preparation in the human frontal cortex. Neuroimage 34, 1209–1219 (2007)

    Google Scholar 

  23. Cunnington, R., Windischberger, C., Deecke, L., Moser, E.: The preparation and execution of self-initiated and externally triggered movement: a study of event-related fMRI. Neuroimage 15, 373–385 (2002)

    CrossRef  Google Scholar 

  24. Cunnington, R., Windischberger, C., Deecke, L., Moser, E.: The preparation and readiness for voluntary movement: a high-field event related fMRI study of the Bereitschafts-BOLD response. Neuroimage 20, 404–412 (2003)

    CrossRef  Google Scholar 

  25. Fridman, E., et al.: The role of the dorsal stream for gesture production. Neuroimage 29, 417–428 (2006)

    CrossRef  Google Scholar 

  26. Holper, L., Scholkmann, F., Shalóm, D.E., Wolf, M.: Extension of mental preparation positively affects motor imagery as compared to motor execution: a functional near-infrared spectroscopy study. Cortex 48, 593–603 (2012)

    CrossRef  Google Scholar 

  27. Suzuki, M., Miyai, I., Ono, T., Kubota, K.: Activities in the frontal cortex and gait performance are modulated by preparation. An fNIRS study. Neuroimage 39, 600–607 (2008)

    CrossRef  Google Scholar 

  28. Lebedev, M.A., Nicolelis, M.A.L.: Brain-machine interfaces: past, present and future. Trends Neurosci. 29, 536–546 (2006)

    CrossRef  Google Scholar 

  29. Fonseca, C., et al.: A novel dry active electrode for EEG recording. IEEE Trans. Biomed. Eng. 54(1), 162–165 (2007)

    CrossRef  Google Scholar 

  30. Popescu, F., Fazli, S., Badower, Y., Blankertz, B., Müller, K.R.: Single trial classification of motor imagination using 6 dry EEG electrodes. PLoS ONE 2, 637 (2007)

    CrossRef  Google Scholar 

  31. Shin, J., von Lühmann, A., Blankertz, B., Kim, D.-W., Hwang, H.-J., Müller, K.-R.: Open access dataset for EEG+NIRS single-trial classification. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1735–1745 (2017)

    Google Scholar 

  32. Blankertz, B., Tangermann, M., Vidaurre, C., Fazli, S., Sannelli, C., Haufe, S., Maeder, C., Ramsey, L.E., Sturm, I., Curio, G., Mueller, K.R.: The Berlin brain-computer interface: non-medical uses of BCI technology. Front. Neurosci. 4, 198 (2010)

    CrossRef  Google Scholar 

  33. Iadecola, C.: Neurovascular regulation in the normal brain and in Alzheimer’s disease. Nat. Rev. Neurosci. 5, 347–360 (2004)

    CrossRef  Google Scholar 

  34. Roy, C.S.C., Sherrington, C.S.C.: On the regulation of the blood-supply of the brain. J. Physiol. 1, 85–108 (1890)

    CrossRef  Google Scholar 

  35. Takeuchi, M., et al.: Brain cortical mapping by simultaneous recording of functional near infrared spectroscopy and electroencephalograms from the whole brain during right median nerve stimulation. Brain Topogr. 22, 197–214 (2009)

    CrossRef  Google Scholar 

  36. Vidaurre, C., Blankertz, B.: Towards a cure for BCI illiteracy. Brain Topogr. 23, 194–198 (2009)

    CrossRef  Google Scholar 

  37. Vidaurre, C., Sannelli, C., Müller, K.R., Blankertz, B.: Machinelearning-based coadaptive calibration for brain–computer interfaces. Neural Comput. 23, 791–816 (2011)

    CrossRef  Google Scholar 

  38. Fazli, S., et al.: Enhanced performance by a hybrid NIRS–EEG brain computer interface. Neuroimage 59(1), 519–529 (2012)

    CrossRef  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Pakistan and European Union (EU)’s Horizon 2020, Research and Innovation Staff Exchange Evaluations (RISE) under grant agreement No 823904 - ENHANCE project (MSCA-RISE 823904) for technical support and funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmed Husnain Johar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Johar, A.H. et al. (2021). Investigation of EEG Correlate in NIRS Signal for BCI. In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_42

Download citation