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A comprehensive review of deep learning power in steady-state visual evoked potentials

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Abstract

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with.

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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

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

    A. S. Albahri & Ghadeer Ghazi Shayea

  3. College of Business Informatics, University of Information Technology and Communications (UoITC), Baghdad, Iraq

    Rula A. Hamid

  4. Department of Management Information Systems, College of Administration and Economics, University of Mosul, Mosul, Iraq

    M. A. Alsalem

  5. Australian Technical and Management College, Melbourne, Australia

    O. S. Albahri

  6. Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq

    O. S. Albahri

  7. Applied Science Research Center, Applied Science Private University, Amman, Jordan

    A. H. Alamoodi

  8. Department of Oil and Gas Engineering, Basra University of Oil and Gas, Basra, 1004, Iraq

    Raad Z. Homod

  9. Ministry of Education, Thi-Qar Education Directorate, Thi Qar, Iraq

    Ali M. Duhaim

  10. MEU Research Unit, Middle East University, Amman, Jordan

    A. H. Alamoodi

  11. University of Information Technology and Communications (UoITC), 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. A comprehensive review of deep learning power in steady-state visual evoked potentials. Neural Comput & Applic 36, 16683–16706 (2024). https://doi.org/10.1007/s00521-024-10143-z

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