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Dynamic decision-making framework for benchmarking brain–computer interface applications: a fuzzy-weighted zero-inconsistency method for consistent weights and VIKOR for stable rank

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Abstract

Benchmarking brain–computer interface (BCI) applications, considering all available smart training environment (STE) criteria, is a challenging task due to the following issues: inconsistent weights, static ranks and ranking stability measurements. Therefore, this study aims to develop a dynamic decision-making framework for benchmarking BCI applications based on STE criteria through three integrated phases. In the first phase, the adaptivity of the decision matrix is identified concerning two dimensions: 27 BCI applications as alternatives and 10 STE criteria. In the second phase, the consistency of weights is evaluated and constructed to each STE criterion via the fuzzy-weighted zero-inconsistency (FWZIC) method and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method for benchmarking the BCI applications. In the third phase, four sensitive scenarios are developed for measuring the consistency of the STE criteria’s weights and the ranking performance of the BCI applications. The experimental result shows that the ‘ease of use’ STE criterion obtains a high-affected weight with a value of 0.13, while other criteria, augmented reality, hybrid and desktop use (stationary), obtain less weight with a 0.075 value. Additionally, BCI applications A5 and A6 are robust and stable among the others based on the consistency of weights concerning the four scenarios, and they are further candidates to be deployed in real-life applications. The overall ranking results are stable and less affected when applied to the four sensitive scenarios due to the robustness of the integrated FWZIC-VIKOR method of the proposed dynamic framework. The outcome of this framework is objectively validated in terms of five groups, and the ranking results are reliable and the closest to the decision-makers' viewpoints. The proposed framework considers a good solution for choosing a dependable application to support the user and community of BCI systems with a stable STE environment.

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Funding

A.S. Albahri  would like to acknowledge the support of Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq.

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Authors and Affiliations

  1. Department of Computer Science, Computer Science and Mathematics College, Tikrit University (TU), Tikrit, Iraq

    Z. T. Al-qaysi, M. A. Ahmed & Mahmood M. Salih

  2. Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq

    A. S. Albahri

  3. Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq

    A. S. Albahri

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  1. Z. T. Al-qaysi
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Al-qaysi, Z.T., Albahri, A.S., Ahmed, M.A. et al. Dynamic decision-making framework for benchmarking brain–computer interface applications: a fuzzy-weighted zero-inconsistency method for consistent weights and VIKOR for stable rank. Neural Comput & Applic 36, 10355–10378 (2024). https://doi.org/10.1007/s00521-024-09605-1

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