Skip to main content

Advertisement

Introducing a fuzzy task-related connectivity index for BCI systems applications

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Brain connectivity measures the relationship between brain regions that occur in response to a task (task-related) or in resting state (intrinsic). Although differences in resting-state connectivity are frequently attributed to task-related brain activity, there is currently no valid criterion for directly evaluating task-related connectivity from signal data. This study aims to establish a fuzzy index based on fuzzy Subsethood (FSH) measure for estimating task-related connectivity. First, two sets of simulated time series with linear and nonlinear interactions were used to evaluate the proposed approach. The results of FSH method on simulated data indicate that the fuzzy Subsethood criterion could identify and estimate task-related connectivity. Afterward, an EEG dataset with an SSVEP task in a mobile BCI system was used to test the approach using real-world data. To address the volume conduction effect and the problem of fake connectivity, the signals were first mapped into the domain of independent components using the Fourier-ICA algorithm, and brain connectivity among brain sources was investigated. The FSH approach was then used to estimate task-related connectivity, and a pattern recognition algorithm was used to determine stimulus frequency. The results demonstrated that the FSH approach is robust when modifying the user's speed. Furthermore, at a speed of 2 m/s, the FSH approach significantly outperformed the standard CCA method (79.48% vs. 55%, respectively; p value = 0.0075). Based on the findings, it is possible to conclude that the FSH index is a suitable measure for estimating task-related connectivity.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The datasets analyzed during the current study are available in the [Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running] repository, [https://doi.org/10.17605/OSF.IO/R7S9B].

References

  • Alruwaili M, Alruwaili R, Kumar UA, Albarrak AM, Ali NH, Basri R (2023) Human emotion recognition based on brain signal analysis using fuzzy neural network. Soft Comput. https://doi.org/10.1007/s00500-023-08224-7

    Article  Google Scholar 

  • Andrea B-M, Felipe O-E, Alberto R-GC (2021) Ordering of functions according to multiple fuzzy criteria: application to denoising electroencephalography. Soft Comput 25(13):8573–8593

    Google Scholar 

  • Bakhshayesh H, Fitzgibbon SP, Janani AS, Grummett TS, Pope KJ (2019) Detecting connectivity in EEG: a comparative study of data-driven effective connectivity measures. Comput Biol Med 111:103329

    Google Scholar 

  • Delorme A, Mullen T, Kothe C, Akalin Acar Z, Bigdely-Shamlo N, Vankov A, Makeig S (2011) EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. Comput Intell Neurosci 2011:130714

    Google Scholar 

  • Dini H, Sendi MS, Sui J, Fu Z, Espinoza R, Narr KL, Qi S, Abbott CC, van Rooij SJ, Riva-Posse P (2021) Dynamic functional connectivity predicts treatment response to electroconvulsive therapy in major depressive disorder. Front Hum Neurosci 15:341

    Google Scholar 

  • Elsayed NE, Tolba AS, Rashad MZ, Belal T, Sarhan S (2021) A deep learning approach for brain computer interaction-motor execution EEG signal classification. IEEE Access 9:101513–101529

    Google Scholar 

  • Fan J, Xie W, Pei J (1999) Subsethood measure: new definitions. Fuzzy Sets Syst 106(2):201–209

    MathSciNet  Google Scholar 

  • Farokhzadi M, Hossein-Zadeh G-A, Soltanian-Zadeh H (2018) Nonlinear effective connectivity measure based on adaptive neuro fuzzy inference system and granger causality. Neuroimage 181:382–394

    Google Scholar 

  • Hallett M, de Haan W, Deco G, Dengler R, Di Iorio R, Gallea C, Gerloff C, Grefkes C, Helmich RC, Kringelbach ML (2020) Human brain connectivity: Clinical applications for clinical neurophysiology. Clin Neurophysiol 131(7):1621–1651

    Google Scholar 

  • Horwitz B (2003) The elusive concept of brain connectivity. Neuroimage 19(2):466–470

    Google Scholar 

  • Ji W, Qiu J (2022) Observer-based output feedback control of nonlinear 2-D systems via fuzzy-affine models. IEEE Trans Instrum Meas 71:1–10

    Google Scholar 

  • Ji W, Qiu J, Su S-F, Zhang H (2022) Fuzzy Observer-Based Output Feedback Control of Continuous-Time Nonlinear Two-Dimensional Systems. IEEE Trans Fuzzy Syst 31:1391–1400

    Google Scholar 

  • Kiani M, Andreu-Perez J, Hagras H, Papageorgiou EI, Prasad M, Lin C-T (2019) Effective brain connectivity for fNIRS with fuzzy cognitive maps in neuroergonomics. IEEE Trans Cogn Dev Syst 14(1):50–63

    Google Scholar 

  • Kosko B, Burgess JC (1998) Neural networks and fuzzy systems. Acoustical Society of America, Melville

    Google Scholar 

  • Lee Y-E, Shin G-H, Lee M, Lee S-W (2021) Mobile BCI dataset of scalp-and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running. Sci Data 8(1):1–12

    Article  Google Scholar 

  • Li K, Guo L, Nie J, Li G, Liu T (2009) Review of methods for functional brain connectivity detection using fMRI. Comput Med Imaging Graph 33(2):131–139

    Article  Google Scholar 

  • Mehdizadehfar V, Ghassemi F, Fallah A, Mohammad-Rezazadeh I, Pouretemad H (2020) Brain connectivity analysis in fathers of children with autism. Cogn Neurodyn 14(6):781–793

    Article  Google Scholar 

  • Norcia AM, Appelbaum LG, Ales JM, Cottereau BR, Rossion B (2015) The steady-state visual evoked potential in vision research: a review. J vis 15(6):4–4

    Article  Google Scholar 

  • Phang C-R, Ko L-W (2020) Intralobular and interlobular parietal functional network correlated to MI-BCI performance. IEEE Trans Neural Syst Rehabil Eng 28(12):2671–2680

    Article  Google Scholar 

  • Pillai AS, McAuliffe D, Lakshmanan BM, Mostofsky SH, Crone NE, Ewen JB (2018) Altered task-related modulation of long-range connectivity in children with autism. Autism Res 11(2):245–257

    Google Scholar 

  • Rahimi M, Davoodi R, Moradi MH (2020) Deep fuzzy model for non-linear effective connectivity estimation in the intuition of consciousness correlates. Biomed Signal Process Control 57:101732

    Google Scholar 

  • Rostami E, Ghassemi F, Tabanfar Z (2022) Canonical correlation analysis of task related components as a noise-resistant method in Brain-computer interface speller systems based on steady-state visual evoked potential. Biomed Signal Process Control 73:103449

    Google Scholar 

  • Roy A (2018) Examining dynamic functional relationships in a pathological brain using evolutionary computation. Soft Comput 22(7):2341–2368

    Google Scholar 

  • Ruiz-Gómez SJ, Hornero R, Poza J, Maturana-Candelas A, Pinto N, Gómez C (2019) Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Appl Alzheimer’s Disease Continuum J Neural Eng 16(6):066019

    Google Scholar 

  • Salvo JJ, Holubecki AM, Braga RM (2021) Correspondence between functional connectivity and task-related activity patterns within the individual. Curr Opin Behav Sci 40:178–188

    Google Scholar 

  • Shakib SS, Ghassemi F (2022) Investigating the effect of stimulus type on electroencephalogram signal in a brain-computer interface system with interaction error. Front Biomed Technol 9(2):127–133

    Google Scholar 

  • Stam C (2010) Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders. J Neurol Sci 289(1–2):128–134

    Google Scholar 

  • Tabanfar Z, Firoozabadi M, Shankayi Z, Sharifi G (2022a) Screening of Brain Tumors Using Functional Connectivity Patterns of Steady-State Visually Evoked Potentials. Brain Connect 12:883–891

    Google Scholar 

  • Tabanfar Z, Ghassemi F, Moradi MH (2022b) Estimating brain periodic sources activities in steady-state visual evoked potential using local Fourier independent component analysis. Biomed Signal Process Control 71:103162

    Google Scholar 

  • Tabanfar Z, Ghassemi F, Moradi MH (2023) A subject-independent SSVEP-based BCI target detection system based on fuzzy ordering of EEG task-related components. Biomed Signal Process Control 79:104171

    Google Scholar 

  • Talebi N, Nasrabadi AM, Mohammad-Rezazadeh I (2019) Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation. Med Biol Eng Comput 57(9):1947–1959

    Google Scholar 

  • Trujillo JP, Gerrits NJ, Veltman DJ, Berendse HW, van der Werf YD, van den Heuvel OA (2015) Reduced neural connectivity but increased task-related activity during working memory in de novo P arkinson patients. Hum Brain Mapp 36(4):1554–1566

    Google Scholar 

  • Wei Y, Jing X, Zheng WX, Qiu J, Karimi HR (2018) A new design of asynchronous observer-based output-feedback control for piecewise-affine systems. IEEE Control Syst Lett 3(2):338–343

    MathSciNet  Google Scholar 

  • Weisstein EW (2004) Bonferroni correction. https://mathworld.wolfram.com/

  • Zimmermann HJ (2010) Fuzzy set theory. Wiley Interdiscip Rev: Comput Stat 2(3):317–332

    MathSciNet  Google Scholar 

Download references

Funding

The author(s) received no specific funding for this work.

Author information

Authors and Affiliations

Authors

Contributions

ZT contributed to conceptualization; ZT contributed to methodology; ZT, FG, and MHM contributed to investigation; ZT contributed to writing—original draft; FG and MHM contributed to writing—review and editing; FG and MHM contributed to supervision.

Corresponding author

Correspondence to Farnaz Ghassemi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tabanfar, Z., Ghassemi, F. & Moradi, M.H. Introducing a fuzzy task-related connectivity index for BCI systems applications. Soft Comput 28, 8849–8860 (2024). https://doi.org/10.1007/s00500-023-09075-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09075-y

Keywords