Skip to main content

Effectiveness of Employing Multimodal Signals in Removing Artifacts from Neuronal Signals: An Empirical Analysis

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12241))

Abstract

Neurophysiological recordings, particularly neuronal signals recorded using multi-site neuronal probes or multielectrode arrays, are often contaminated with unwanted signals or artifacts from external or internal sources. Almost all types of neuronal signals including electroencephalogram (EEG), electrocorticogram (ECoG), local field potentials (LFP), and spikes very often suffer greatly from these artifacts and require extensive amount of processing to get rid of them. Despite considerable efforts in developing sophisticated methods to detect and remove these artifacts, it often appears a challenging task due to the inherent similar spatio-temporal properties of the artifacts and the recorded signals. In such cases, the incorporation of another modality can facilitate and improve the detection of these artifacts, and remove them. This paper focuses on the EEG signal and empirically analyses the role played by the addition of a new modality (e.g., cardiac signals, muscular signals, ocular signals, and motion signals) in detecting artifacts from EEG signals.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bang, J.W., Choi, J.S., Park, K.R.: Noise reduction in brainwaves by using both EEG signals and frontal viewing camera images. Sensors 13(5), 6272–6294 (2013)

    Article  Google Scholar 

  2. Barra, S., Fraschini, M., Casanova, A., Castiglione, A., Fenu, G.: Physiounicadb: a dataset of EEG and ECG simultaneously acquired. Pattern Recogn. Lett. 126, 119–122 (2019)

    Article  Google Scholar 

  3. Daly, I., Billinger, M., Scherer, R., Müller-Putz, G.: On the automated removal of artifacts related to head movement from the EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 21(3), 427–434 (2013)

    Article  Google Scholar 

  4. Dora, C., Biswal, P.K.: Robust ECG artifact removal from EEG using continuous wavelet transformation and linear regression. In: 2016 International Conference on Signal Processing and Communications (SPCOM), pp. 1–5. IEEE (2016)

    Google Scholar 

  5. Fabietti, M., et al.: Neural network-based artifact detection in local field potentials recorded from chronically implanted neural probes. In: Proceedings of IJCNN, pp. 1–8 (2020)

    Google Scholar 

  6. Ghosh, R., Sinha, N., Biswas, S.K.: Automated eye blink artefact removal from EEG using support vector machine and autoencoder. IET Signal Proc. 13(2), 141–148 (2018)

    Article  Google Scholar 

  7. Grimaldi, G., Manto, M., Jdaoudi, Y.: Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs. F1000Research 2, 282 (2013)

    Article  Google Scholar 

  8. Gwin, J.T., Gramann, K., Makeig, S., Ferris, D.P.: Removal of movement artifact from high-density EEG recorded during walking and running. J. Neurophysiol. 103(6), 3526–3534 (2010)

    Article  Google Scholar 

  9. Issa, M.F., Tuboly, G., Kozmann, G., Juhasz, Z.: Automatic ECG artefact removal from EEG signals. Measur. Sci. Rev. 19(3), 101–108 (2019)

    Article  Google Scholar 

  10. Kemp, B.: The sleep-EDF database. World Wide Web. http://www.physionet.org/physiobank/database/sleep-edf/. Accessed August 2009

  11. Kim, B.H., Chun, J., Jo, S.: Dynamic motion artifact removal using inertial sensors for mobile BCI. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 37–40. IEEE (2015)

    Google Scholar 

  12. Kline, J.E., Huang, H.J., Snyder, K.L., Ferris, D.P.: Isolating gait-related movement artifacts in electroencephalography during human walking. J. Neural Eng. 12(4), 046022 (2015)

    Article  Google Scholar 

  13. Koessler, L., et al.: Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. Neuroimage 46(1), 64–72 (2009)

    Article  Google Scholar 

  14. Krishnaswamy, P., Bonmassar, G., Poulsen, C., Pierce, E.T., Purdon, P.L., Brown, E.N.: Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression. Neuroimage 128, 398–412 (2016)

    Article  Google Scholar 

  15. Lanquart, J.P., Dumont, M., Linkowski, P.: QRS artifact elimination on full night sleep EEG. Med. Eng. Phys. 28(2), 156–165 (2006)

    Article  Google Scholar 

  16. Looney, D., Goverdovsky, V., Kidmose, P., Mandic, D.P.: Subspace denoising of EEG artefacts via multivariate EMD. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4688–4692. IEEE (2014)

    Google Scholar 

  17. Mahmud, M., Cecchetto, C., Vassanelli, S.: An automated method for characterization of evoked single-trial local field potentials recorded from rat barrel cortex under mechanical whisker stimulation. Cogn. Comput. 8(5), 935–945 (2016). https://doi.org/10.1007/s12559-016-9399-3

    Article  Google Scholar 

  18. Mahmud, M., Girardi, S., Maschietto, M., Vassanelli, S.: An automated method to remove artifacts induced by microstimulation in local field potentials recorded from rat somatosensory cortex. In: Proceedings BRC, pp. 1–4 (2012). https://doi.org/10.1109/BRC.2012.6222169

  19. Mahmud, M., Bertoldo, A., Vassanelli, S.: EEG based brain-machine interfacing: Navigation of mobile robotic device. In: Bedkowski, J. (ed.) Mobile Robots-Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training. IntechOpen (2011)

    Google Scholar 

  20. Mahmud, M., Girardi, S., Maschietto, M., Rahman, M.M., Bertoldo, A., Vassanelli, S.: Slow stimulus artifact removal through peak-valley detection of neuronal signals recorded from somatosensory cortex by high resolution brain-chip interface. In: Dossel, O., Schlegel, W.C. (eds.) World Congress on Medical Physics and Biomedical Engineering. IFMBE Proceedings, vol. 25/4. Springer, Berlin (2009). https://doi.org/10.1007/978-3-642-03882-2_547

    Chapter  Google Scholar 

  21. Mahmud, M., Hawellek, D., Bertoldo, A.: EEG based brain-machine interface for navigation of robotic device. In: 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 168–172. IEEE (2010)

    Google Scholar 

  22. Mahmud, M., Hawellek, D., Valjamae, A.: A brain-machine interface based on EEG: extracted alpha waves applied to mobile robot. In: 2009 Advanced Technologies for Enhanced Quality of Life, pp. 28–31. IEEE (2009)

    Google Scholar 

  23. Mahmud, M., Hussain, A.: Towards reduced EEG based brain-computer interfacing for mobile robot navigation. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013. LNCS (LNAI), vol. 8266, pp. 413–422. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45111-9_36

    Chapter  Google Scholar 

  24. Mahmud, M., Travalin, D., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: An automated classification method for single sweep local field potentials recorded from rat barrel cortex under mechanical whisker stimulation. J. Med. Biol. Eng. 32(6), 397–404 (2012)

    Article  Google Scholar 

  25. Mahmud, M., Vassanelli, S.: Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges. Front. Neurosci. 10, 248 (2016)

    Google Scholar 

  26. Mannan, M.M.N., Kim, S., Jeong, M.Y., Kamran, M.A.: Hybrid EEG–eye tracker: automatic identification and removal of eye movement and blink artifacts from electroencephalographic signal. Sensors 16(2), 241 (2016)

    Article  Google Scholar 

  27. Maurandi, V., Rivet, B., Phlypo, R., Guérin–Dugué, A., Jutten, C.: Multimodal approach to remove ocular artifacts from EEG signals using multiple measurement vectors. In: Tichavský, P., Babaie-Zadeh, M., Michel, O.J.J., Thirion-Moreau, N. (eds.) LVA/ICA 2017. LNCS, vol. 10169, pp. 563–573. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53547-0_53

    Chapter  Google Scholar 

  28. McIntosh, J.R., Yao, J., Hong, L., Faller, J., Sajda, P.: Ballistocardiogram artifact reduction in simultaneous EEG-FMRI using deep learning. arXiv preprint arXiv:1910.06659 (2019)

  29. Muthukumaraswamy, S.: High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations. Front. Human Neurosci. 7, 138 (2013)

    Article  Google Scholar 

  30. Oliveira, A.S., Schlink, B.R., Hairston, W.D., König, P., Ferris, D.P.: A channel rejection method for attenuating motion-related artifacts in EEG recordings during walking. Front. Neurosci. 11, 225 (2017)

    Article  Google Scholar 

  31. Onikura, K., Iramina, K.: Evaluation of a head movement artifact removal method for EEG considering real-time processing. In: 2015 8th Biomedical Engineering International Conference (BMEiCON), pp. 1–4. IEEE (2015)

    Google Scholar 

  32. O’Regan, S., Faul, S., Marnane, W.: Automatic detection of EEG artefacts arising from head movements. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 6353–6356. IEEE (2010)

    Google Scholar 

  33. O’Regan, S., Faul, S., Marnane, W.: Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals. Med. Eng. Phys. 35(7), 867–874 (2013)

    Article  Google Scholar 

  34. Plöchl, M., Ossandón, J.P., König, P.: Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data. Front. Hum. Neurosci. 6, 278 (2012)

    Article  Google Scholar 

  35. Quax, S.C., Dijkstra, N., van Staveren, M.J., Bosch, S.E., van Gerven, M.A.: Eye movements explain decodability during perception and cued attention in MEG. Neuroimage 195, 444–453 (2019)

    Article  Google Scholar 

  36. Raif, P., Mahmud, M., Hussain, A., Klos-Witkowska, A., Suchanek, R.: A brain-computer interface test-bench based on EEG signals for research and student training. In: 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), pp. 46–50. IEEE (2013)

    Google Scholar 

  37. Rezaei, M., Mohammadi, H., Khazaie, H.: EEG/EOG/EMG data from a cross sectional study on psychophysiological insomnia and normal sleep subjects. Data in brief 15, 314–319 (2017)

    Article  Google Scholar 

  38. Rivet, B., Duda, M., Guérin-Dugué, A., Jutten, C., Comon, P.: Multimodal approach to estimate the ocular movements during EEG recordings: a coupled tensor factorization method. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6983–6986. IEEE (2015)

    Google Scholar 

  39. Samadi, M.R.H., Zakeri, Z., Cooke, N.: VOG-enhanced ICA for removing blink and eye-movement artefacts from EEG. In: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 603–606. IEEE (2016)

    Google Scholar 

  40. Schwabedal, J.T., Sippel, D., Brandt, M.D., Bialonski, S.: Automated classification of sleep stages and EEG artifacts in mice with deep learning. arXiv preprint arXiv:1809.08443 (2018)

  41. Sweeney, K.T., Ayaz, H., Ward, T.E., Izzetoglu, M., McLoone, S.F., Onaral, B.: A methodology for validating artifact removal techniques for physiological signals. IEEE Trans. Inf Technol. Biomed. 16(5), 918–926 (2012)

    Article  Google Scholar 

  42. Sweeney, K.T., Leamy, D.J., Ward, T.E., McLoone, S.: Intelligent artifact classification for ambulatory physiological signals. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 6349–6352. IEEE (2010)

    Google Scholar 

  43. Sweeney, K.T., Ward, T.E., McLoone, S.F.: Artifact removal in physiological signals–practices and possibilities. IEEE Trans. Inf Technol. Biomed. 16(3), 488–500 (2012)

    Article  Google Scholar 

  44. Tavildar, S., Ashrafi, A.: Application of multivariate empirical mode decomposition and canonical correlation analysis for EEG motion artifact removal. In: 2016 Conference on Advances in Signal Processing (CASP), pp. 150–154. IEEE (2016)

    Google Scholar 

  45. Urigüen, J.A., Garcia-Zapirain, B.: EEG artifact removal–state-of-the-art and guidelines. J. Neural Eng. 12(3), 031001 (2015)

    Article  Google Scholar 

  46. Vassanelli, S., Mahmud, M.: Trends and challenges in neuroengineering: toward “intelligent” neuroprostheses through brain-“brain inspired systems” communication. Front. Neurosci. 10, 438 (2016)

    Article  Google Scholar 

  47. Wang, K., Li, W., Dong, L., Zou, L., Wang, C.: Clustering-constrained ICA for ballistocardiogram artifacts removal in simultaneous EEG-FMRI. Front. Neurosci. 12, 59 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mufti Mahmud .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fabietti, M., Mahmud, M., Lotfi, A. (2020). Effectiveness of Employing Multimodal Signals in Removing Artifacts from Neuronal Signals: An Empirical Analysis. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59277-6_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59276-9

  • Online ISBN: 978-3-030-59277-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics