Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter October 29, 2020

Initial study on changes in activity of brain waves during audio stimulation using noninvasive brain–computer interfaces: choosing the appropriate filtering method

  • Natalia Browarska EMAIL logo , Aleksandra Kawala-Sterniuk and Jarosław Zygarlicki

Abstract

Objectives

In this paper series of experiments were carried out in order to check the influence of various sounds on human concentration during visually stimulated tasks performance.

Methods

The obtained data was filtered. For the study purposes various smoothing filters were tested, including Median and Savitzky–Golay Filters; however, median filter only was applied. Implementation of this filter made the obtained data more legible and useful for potential diagnostics purposes. The tests were carried out with the implementation of the Emotiv Flex EEG headset.

Results

The obtained results were promising and complied with the initial assumptions, which stated that the “relax”-phase, despite relaxing sounds stimuli, is strongly affected with the “focus”-phase with distracting sounds, which is clearly visible in the shape of the recorded EEG data.

Conclusions

Further investigations with broader range of subjects is being currently carried out in order to confirm the already obtained results.


Corresponding author: Natalia Browarska, Faculty of Electrical Engineering, Automatic Control and Informatics — Opole University of Technology, Opole, Poland, E-mail:

  1. Research funding: None declared.

  2. Author contributions: All the authors have accepted responsibility for the entire content of this sub-mitted manuscript and approved submission.

  3. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

  4. Ethical Approval: The conducted research is not related to either human or animal use.

References

1. Shih, JJ, Krusienski, DJ, Wolpaw, JR. Brain-computer interfaces in medicine. In: Mayo clinic proceedings: Elsevier; 2012, vol. 87:268–79. https://doi.org/10.1016/j.mayocp.2011.12.008.Search in Google Scholar PubMed PubMed Central

2. Kawala-Janik, A. Efficiency evaluation of external environments control using bio-signals. London, UK: University of Greenwich; 2013.Search in Google Scholar

3. Stach, T, Browarska, N, Kawala-Janik, A. Initial study on using emotiv EPOC+ neuroheadset as a control device for picture script-based communicators. IFAC-Papers OnLine 2018;51:180–4. https://doi.org/10.1016/j.ifacol.2018.07.150.Search in Google Scholar

4. Koelsch, S. Neural substrates of processing syntax and semantics in music. Music that works. Vienna: Springer; 2009:143–53 p.10.1007/978-3-211-75121-3_9Search in Google Scholar

5. Teixeira, AR, Tomé, A, Roseiro, L, Gomes, A. Does music help to be more attentive while performing a task? A brain activity analysis. In: 2018 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, Madrid, Spain; 2018:1564–70 p.10.1109/BIBM.2018.8621388Search in Google Scholar

6. Koelsch, S, Siebel, WA. Towards a neural basis of music perception. Trends Cognit Sci 2005;9:578–84. https://doi.org/10.1016/j.tics.2005.10.001.Search in Google Scholar PubMed

7. Bitner, MJ. Servicescapes: the impact of physical surroundings on customers and employees. J Market 1992;56:57–71. https://doi.org/10.2307/1252042.Search in Google Scholar

8. Shih, YN, Huang, RH, Chiang, Hs. Correlation between work concentration level and background music: a pilot study. Work 2009;33:329–33. https://doi.org/10.3233/wor-2009-0880.Search in Google Scholar

9. Kawala-Janik, A, Pelc, M, Podpora, M. Method for EEG signals pattern recognition in embedded systems. Elektronika ir Elektrotechnika 2015;21:3–9. https://doi.org/10.5755/j01.eee.21.3.9918.Search in Google Scholar

10. Kawala-Sterniuk, A, Podpora, M, Pelc, M, Blaszczyszyn, M, Gorzelanczyk, EJ, Martinek, R, et al.. Comparison of smoothing filters in analysis of EEG data for the medical diagnostics purposes. Sensors 2020;20:807. https://doi.org/10.3390/s20030807.Search in Google Scholar PubMed PubMed Central

11. Wierzgała, P, Zapała, D, Wojcik, GM, Masiak, J. Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front Neuroinf 2018;12:78. https://doi.org/10.3389/fninf.2018.00078.Search in Google Scholar PubMed PubMed Central

12. Daly, I, Malik, A, Hwang, F, Roesch, E, Weaver, J, Kirke, A, et al.. Neural correlates of emotional responses to music: an EEG study. Neurosci Lett 2014;573:52–7. https://doi.org/10.1016/j.neulet.2014.05.003.Search in Google Scholar PubMed

13. Geethanjali, B, Adalarasu, K, Rajsekaran, R. Impact of music on brain function during mental task using electroencephalography. Int J Biomed Biol Eng 2012;6:256–60. https://doi.org/10.1016/j.neulet.2014.05.003.Search in Google Scholar

14. Teplan, M, Krakovska, A, Štolc, S. EEG responses to long-term audio–visual stimulation. Int J Psychophysiol 2006;59:81–90. https://doi.org/10.1016/j.ijpsycho.2005.02.005.Search in Google Scholar PubMed

15. Jirayucharoensak, S, Pan-Ngum, S, Israsena, P. EEG-based emotion recognition using deep learn-ing network with principal component based covariate shift adaptation. Sci World J 2014;2014. https://doi.org/10.1155/2014/627892.Search in Google Scholar PubMed PubMed Central

16. Emotiv. Emotiv flex website; 2020 https://www.emotiv.com/epoc-flex.Search in Google Scholar

17. Chatrian, G, Lettich, E, Nelson, P. Ten percent electrode system for topographic studies of spon-taneous and evoked EEG activities. Am J EEG Technol 1985;25:83–92. https://doi.org/10.1080/00029238.1985.11080163.Search in Google Scholar

18. Doppelmayr, M, Weber, E. Effects of SMR and theta/beta neurofeedback on reaction times, spatial abilities, and creativity. J Neurother 2011;15:115–29. https://doi.org/10.1080/10874208.2011.570689.Search in Google Scholar

19. Zoefel, B, Huster, RJ, Herrmann, CS. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage 2011;54:1427–31. https://doi.org/10.1016/j.neuroimage.2010.08.078.Search in Google Scholar PubMed

20. Lansbergen, MM, Arns, M, van Dongen-Boomsma, M, Spronk, D, Buitelaar, JK. The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency. Prog Neuro Psychopharmacol Biol Psychiatr 2011;35:47–52. https://doi.org/10.1016/j.pnpbp.2010.08.004.Search in Google Scholar PubMed

21. Grzechca, D, Szczeponik, A. Comparison of filtering methods for enhanced reliability of a train axle counter system. Sensors 2020;20:2754. https://doi.org/10.3390/s20102754.Search in Google Scholar PubMed PubMed Central

22. Sulaiman, N, Taib, MN, Aris, SAM, Hamid, NHA, Lias, S, Murat, ZH. Stress features identification from EEG signals using EEG asymmetry & spectral centroids techniques. In: 2010 IEEE EMBS conference on biomedical engineering and sciences (IECBES). IEEE, Kuala Lumpur, Malaysia; 2010:417–21 p.10.1109/IECBES.2010.5742273Search in Google Scholar

23. Zheng, WL, Zhu, JY, Peng, Y, Lu, BL. EEG-based emotion classification using deep belief networks. In: 2014 IEEE international conference on multimedia and expo (ICME). IEEE, Chengdu, China; 2014:1–6 p.10.1109/ICME.2014.6890166Search in Google Scholar

24. Jena, SK. Examination stress and its effect on EEG. Int J Med Sci Publ Health 2015;11:1493–7. https://doi.org/10.5455/ijmsph.2015.23042015308.Search in Google Scholar

25. Seo, SH, Lee, JT, Crisan, M. Stress and EEG. Converg Hybrid Inf Technol 2010;1:413–24. https://doi.org/10.5772/9651.Search in Google Scholar

26. Kim, WS, Yoon, YR, Kim, KH, Jho, MJ, Lee, ST. Asymmetric activation in the prefrontal cortex by sound-induced affect. Percept Mot Skills 2003;97:847–54. https://doi.org/10.2466/pms.2003.97.3.847.Search in Google Scholar PubMed

27. Zentner, M, Grandjean, D, Scherer, KR. Emotions evoked by the sound of music: characterization, classification, and measurement. Emotion 2008;8:494. https://doi.org/10.1037/1528-3542.8.4.494.Search in Google Scholar PubMed

28. Lin, YP, Wang, CH, Jung, TP, Wu, TL, Jeng, SK, Duann, JR, et al.. EEG-based emotion recognition in music listening. IEEE (Inst Electr Electron Eng) Trans Biomed Eng 2010;57:1798–806. https://doi.org/10.1109/TBME.2010.2048568.Search in Google Scholar PubMed

29. Spezialetti, M, Cinque, L, Tavares, JMR, Placidi, G. Towards EEG-based BCI driven by emotions for addressing BCI-Illiteracy: a meta-analytic review. Behav Inf Technol 2018;37:855–71. https://doi.org/10.1080/0144929x.2018.1485745.Search in Google Scholar

30. Jebelli, H, Hwang, S, Lee, S. EEG-based workers’ stress recognition at construction sites. Autom ConStruct 2018;93:315–24. https://doi.org/10.1016/j.autcon.2018.05.027.Search in Google Scholar

31. Gorzelańczyk, EJ, Podlipniak, P, Walecki, P, Karpiński, M, Tarnowska, E. Pitch syntax violations are linked to greater skin conductance changes, relative to timbral violations–the predictive role of the reward system in perspective of cortico–subcortical loops. Front Psychol 2017;8:586. https://doi.org/10.3389/fpsyg.2017.00586.Search in Google Scholar PubMed PubMed Central

32. Jin, J, Chen, Z, Xu, R, Miao, Y, yu Wang, X, Jung, TP. Developing a novel tactile P300 brain-computer interface with a cheeks-stim paradigm. IEEE Trans Biomed Eng 2020:2585–93. https://doi.org/10.1109/TBME.2020.2965178.Search in Google Scholar PubMed

33. Jin, J, Li, S, Daly, I, Miao, Y, Liu, C, Wang, X, et al.. The study of generic model set for reducing calibration time in P300-based brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 2019;28:3–12. https://doi.org/10.1109/TNSRE.2019.2956488.Search in Google Scholar PubMed

34. Yang, W, Guo, A, Li, Y, Qiu, J, Li, S, Yin, S, et al.. Audio-visual spatiotemporal perceptual training enhances the P300 component in healthy older adults. Front Psychol 2018;9:2537. https://doi.org/10.3389/fpsyg.2018.02537.Search in Google Scholar PubMed PubMed Central

Received: 2020-08-18
Accepted: 2020-10-12
Published Online: 2020-10-29

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 26.4.2024 from https://www.degruyter.com/document/doi/10.1515/bams-2020-0051/html
Scroll to top button