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