تحلیل راهبردی حوزه‌ی فناوری‌های عصبی در کشور

نوع مقاله : ‌علمی

نویسندگان

1 پژوهشکده آماد و فناوریهای دفاعی و پدافند غیرعامل، دانشگاه و پژوهشگاه عالی دفاع ملی و تحقیقات‌ راهبردی، تهران، ایران

2 استادیار دانشگاه عالی دفاع ملی و تحقیقات راهبردی، پژوهشکده آماد، گروه فناوری دفاعی، میز علوم و فناوری‌های شناختی

3 میز علوم و فناوری های شناختی، پژوهشکده آماد، دانشگاه عالی دفاع ملی

چکیده

حوزه‌ی علوم مغزی به دلیل ارتباط مستقیم با سلامت انسان و نیز کاربردهای فناورانه متنوع آن، از اهمیت فراوانی برخوردار است. فناوری‌های عصبی دارای دورنمای بسیار روشنی هستند و به همین دلیل، کشورهای توسعه یافته با اختصاص بودجه‌های قابل توجه در این زمینه پیش‌رو می‌باشند. به علت نوظهور بودن این فناوری‌ها، تا کنون تحلیل راهبردی مناسبی برای این حوزه در کشور ارائه نشده است. هدف این پژوهش تعیین سطوح آمادگی فناوری و شناسایی و اولویت‌بندی راهبردهای متناسب با وضعیت کشور در حوزه‌ی فناوری‌‌های عصبی است. برای نیل به این هدف، از توزیع پرسش‌نامه بین جامعه‌‌ی آماری شامل 40 خبره‌ی حوزه‌ی فناوری‌های عصبی استفاده شده است. در قسمت تحلیل داده‌ها با استفاده از روش ماتریس SWOT وزن‌دهی به عوامل خارجی و داخلی صورت گرفته و جایگاه راهبردی کشور مشخص می‌گردد. سپس مقایسه زوجی بین راهبردهای مربوطه انجام می‌شود و در انتها، ماتریس برنامه‌ریزی راهبردی کمی راهبردهای منتخب را بر اساس جذابیت، اولویت‌بندی می‌نماید.
با توجه به نتایج بدست آمده، کشورمان در زیرشاخه‌ی واسط مغز به رایانه دارای بیش‌ترین سطح آمادگی فناوری(سطح 6) و در زیر شاخه‌ی واسط مغز به مغز دارای کمترین سطح آمادگی فناوری (سطح1) می‌باشد. همچنین تحلیل راهبردی عوامل داخلی و خارجی حکایت از آن دارد که ایران در حوزه‌ی فناوری‌های عصبی در جایگاه راهبردی محافظه‌کارانه(ضعف-فرصت) قرار گرفته است. 10 راهبرد مرتبط با این جایگاه به صورت مقایسه زوجی مورد ارزیابی قرار می‌گیرند و پس از آن 5 راهبرد منتخب در این مرحله، با استفاده از ماتریس برنامه‌ریزی راهبردی کمی اولویت‌بندی می‌شوند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Strategic analysis of neurotechnologies in Iran

نویسندگان [English]

  • Ali Toraby 1
  • Hanif Kazerooni 2
  • Hossein Hassanpoor 3
1 Supreme National Defense University, Tehran, Iran
2 Assistant Professor, National Defense and Strategic Research University
3 Assistant Professor,Supreme National Defense University, Tehran, Iran
چکیده [English]

The field of neurotechnology has a bright future ahead of it due to its direct impact on human health and the various technological applications. Consequently, this field is of great importance to many developed countries. As far as we know, since most of the neurotechnologies are emerging technologies, there is no proper strategic analysis for this field in the country. The purpose of this study is to determine the technology readiness level (TRL) for the branches of neurotechnology and identify and prioritize the appropriate strategies by evaluating the current state of the country in the neurotechnology field. To achieve this goal, questionnaires are distributed among 40 experts in neuroscience and neural technologies. In the strategy prioritization and data analysis step, the SWOT matrix-based method is used; then, the weights of internal and external factors are determined. After performing a pairwise comparison for selecting the top 5 strategies, a quantitative strategic planning matrix (QSPM) is used in order to rank the selected ones.
The results indicate that Iran has the highest technology readiness level (level 6) in the brain-to-computer interfaces and the lowest technology readiness level (level 1) belongs to brain-to-brain interfaces. Finally, 5 top strategies which are weakness- opportunity (WO) strategies are determined.

کلیدواژه‌ها [English]

  • Neurotechnology
  • Technology readiness level (TRL)
  • Strategic Analysis
  • SWOT Analysis
  • Quantitative Strategic Planning Matrix (QSPM)
1.       Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). An extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-making. Symmetry, 10(4), 116.
2.       Bahman, S., & Shamsollahi, M. B. (2019). Robot Control Using an InexpensiveP300 Based BCI. Paper presented at the 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME).
3.       Bidaki, R., Mirhosseini, H., Moosavipoor, S. A., & Mirhosseini, S. (2016). Effects of cranial electro stimulation (ces) on modulated brain waves. Journal of Advance Research in Pharmacy & Biological Science (ISSN: 2208-2360), 2(4), 85-81.
4.       Boly, M., Gosseries, O., Massimini, M., & Rosanova, M. (2016). Functional neuroimaging techniques. In The Neurology of Conciousness, 31-47.
5.       Casson, A. J. (2019). Wearable EEG and beyond. Biomedical engineering letters, 9(1), 53-71.
6.       Chai, R., Naik, G. R., Ling, S. H., & Nguyen, H. T. (2017). Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems. Biomedical engineering online, 16(1), 5.
7.       Coogan, C. G., & He, B. (2018). Brain-computer interface control in a virtual reality environment and applications for the internet of things. IEEE Access, 6,10840-10849.
8.       Cooper, D. R., Schindler, P. S., & Sun, J. (2006). Business research methods, 9, McGraw-Hill Irwin New York.
9.       Datascience site. (2019). Retrieved from https://www.statisticshowto.datasciencecentral.com/cronbachs-alpha-spss/
10.   Dayan, P., & Abbott, L. (2003). Theoretical neuroscience: computational and mathematical modeling of neural systems. Journal of Cognitive Neuroscience, 15(1), 154-155.
11.   Eaton, M. L., & Illes, J. (2007). Commercializing cognitive neurotechnology—theethical terrain. Nature biotechnology, 25(4), 393.
12.   Gaugler, J., James, B., Johnson, T., Marin, A., & Weuve, J. (2019). Alzheimer's disease facts and figures. Alzheimers & Dementia, 15(3), 321-387.
13.   Goering, S., & Yuste, R. (2016). On thenecessity of ethical guidelines for novel neurotechnologies. Cell, 167(4), 882-885.
14.   Gürel, E., & Tat, M. (2017). SWOT analysis: a theoretical review. Journal of International Social Research, 10(51).
15.   Hosseini, Z., Ghorbani, Z., & Ebn Ahmady, A.(2015). Face and content validity and reliability assessment of change cycle questionnaire in smokers. Journal of Mashhad Dental School, 39(2), 147-154.
16.   Ienca, M., & Andorno, R. (2017). Towards new human rights in the age of neuroscience and neurotechnology. Life sciences, society and policy, 13(1), 5.
17.   Man, D., & Olchawa, R. (2018). Brain Biophysics: Perception, Consciousness, Creativity. Brain Computer Interface (BCI). Paper presented at the International Scientific Conference BCI 2018 Opole.
18.   Mankins, J. C. (1995). Technology readiness levels. White Paper, April, 6, 1995.
19.   19. Nguyen, J.-P., Nizard, J., Keravel, Y., & Lefaucheur, J.-P. (2011). Invasive brain stimulation for the treatment of neuropathic pain. Nature Reviews Neurology,(12)7, 699.
20.   Panahi, R., Soor, B., Hashemin Nasab, Z., & Qaderpoor, E. (2018). Quarterly Journal of Cognition and Brain, 6, 14-17. Retrieved from www.cogc.ir
21.   Paulus, W. (2011). Transcranial electrical stimulation (tES–tDCS; tRNS, tACS) methods. Neuropsychological rehabilitation, 21(5), 602-617.
22.   Ramadan, R. A., & Vasilakos, A. V. (2017). Brain computer interface: control signals review. Neurocomputing, 223, 26-44.
23.   Roelfsema, P. R., Denys, D., & Klink, P. C. (2018). Mind reading and writing: The future of neurotechnology. Trends in cognitive sciences, 22(7), 598-610.
24.   Sheykhi, M. T. (2017). Aging and the Consequent Alzheimer’s Disease in Iran: An Outlook. of 6. of, 6, 2.
25.   Siar, H., & Teshnehlab, M. (2019). Diagnosing and Classification Tumors and MS Simultaneous of Magnetic Resonance Images Using Convolution Neural Network. Paper presented at the 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).
26.   Slaby, J., & Choudhury, S. (2018). Proposal for a critical neuroscience. In The Palgrave handbook of biology and society, 341-370, Springer.
27.   Snowball, A., Tachtsidis, I., Popescu, T., Thompson, J., Delazer, M., Zamarian, L., & Kadosh, R. C. (2013). Long-term enhancement of brain function and cognition using cognitive training and brain stimulation. Current Biology, 23(11), 987-992.
28.   Sumner, P. J., Bell, I. H., & Rossell, S. L. (2018). A systematic review of the structural neuroimaging correlates of thought disorder. Neuroscience & Biobehavioral Reviews, 84, 299-315.
29.   Tomaschek, K., Olechowski, A., Eppinger, S., & Joglekar, N. (2016). A Survey of Technology Readiness Level Users. Paper presented at the INCOSE International Symposium.
30.   Yoo, S.-S., Kim, H., Filandrianos, E., Taghados, S. J., & Park, S. (2013). Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains. PloS one, 8(4), e60410.
31.   Zulkarnain, A., Wahyuningtias, D., & Putranto, T. (2018). Analysis of IFE, EFE and QSPM matrix on business development strategy. Paper presented at the IOP Conference Series: Earth and Environmental Science.
1.       Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). An extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-making. Symmetry, 10(4), 116.
2.       Bahman, S., & Shamsollahi, M. B. (2019). Robot Control Using an InexpensiveP300 Based BCI. Paper presented at the 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME).
3.       Bidaki, R., Mirhosseini, H., Moosavipoor, S. A., & Mirhosseini, S. (2016). Effects of cranial electro stimulation (ces) on modulated brain waves. Journal of Advance Research in Pharmacy & Biological Science (ISSN: 2208-2360), 2(4), 85-81.
4.       Boly, M., Gosseries, O., Massimini, M., & Rosanova, M. (2016). Functional neuroimaging techniques. In The Neurology of Conciousness, 31-47.
5.       Casson, A. J. (2019). Wearable EEG and beyond. Biomedical engineering letters, 9(1), 53-71.
6.       Chai, R., Naik, G. R., Ling, S. H., & Nguyen, H. T. (2017). Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems. Biomedical engineering online, 16(1), 5.
7.       Coogan, C. G., & He, B. (2018). Brain-computer interface control in a virtual reality environment and applications for the internet of things. IEEE Access, 6,10840-10849.
8.       Cooper, D. R., Schindler, P. S., & Sun, J. (2006). Business research methods, 9, McGraw-Hill Irwin New York.
9.       Datascience site. (2019). Retrieved from https://www.statisticshowto.datasciencecentral.com/cronbachs-alpha-spss/
10.   Dayan, P., & Abbott, L. (2003). Theoretical neuroscience: computational and mathematical modeling of neural systems. Journal of Cognitive Neuroscience, 15(1), 154-155.
11.   Eaton, M. L., & Illes, J. (2007). Commercializing cognitive neurotechnology—theethical terrain. Nature biotechnology, 25(4), 393.
12.   Gaugler, J., James, B., Johnson, T., Marin, A., & Weuve, J. (2019). Alzheimer's disease facts and figures. Alzheimers & Dementia, 15(3), 321-387.
13.   Goering, S., & Yuste, R. (2016). On thenecessity of ethical guidelines for novel neurotechnologies. Cell, 167(4), 882-885.
14.   Gürel, E., & Tat, M. (2017). SWOT analysis: a theoretical review. Journal of International Social Research, 10(51).
15.   Hosseini, Z., Ghorbani, Z., & Ebn Ahmady, A.(2015). Face and content validity and reliability assessment of change cycle questionnaire in smokers. Journal of Mashhad Dental School, 39(2), 147-154.
16.   Ienca, M., & Andorno, R. (2017). Towards new human rights in the age of neuroscience and neurotechnology. Life sciences, society and policy, 13(1), 5.
17.   Man, D., & Olchawa, R. (2018). Brain Biophysics: Perception, Consciousness, Creativity. Brain Computer Interface (BCI). Paper presented at the International Scientific Conference BCI 2018 Opole.
18.   Mankins, J. C. (1995). Technology readiness levels. White Paper, April, 6, 1995.
19.   19. Nguyen, J.-P., Nizard, J., Keravel, Y., & Lefaucheur, J.-P. (2011). Invasive brain stimulation for the treatment of neuropathic pain. Nature Reviews Neurology,(12)7, 699.
20.   Panahi, R., Soor, B., Hashemin Nasab, Z., & Qaderpoor, E. (2018). Quarterly Journal of Cognition and Brain, 6, 14-17. Retrieved from www.cogc.ir
21.   Paulus, W. (2011). Transcranial electrical stimulation (tES–tDCS; tRNS, tACS) methods. Neuropsychological rehabilitation, 21(5), 602-617.
22.   Ramadan, R. A., & Vasilakos, A. V. (2017). Brain computer interface: control signals review. Neurocomputing, 223, 26-44.
23.   Roelfsema, P. R., Denys, D., & Klink, P. C. (2018). Mind reading and writing: The future of neurotechnology. Trends in cognitive sciences, 22(7), 598-610.
24.   Sheykhi, M. T. (2017). Aging and the Consequent Alzheimer’s Disease in Iran: An Outlook. of 6. of, 6, 2.
25.   Siar, H., & Teshnehlab, M. (2019). Diagnosing and Classification Tumors and MS Simultaneous of Magnetic Resonance Images Using Convolution Neural Network. Paper presented at the 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).
26.   Slaby, J., & Choudhury, S. (2018). Proposal for a critical neuroscience. In The Palgrave handbook of biology and society, 341-370, Springer.
27.   Snowball, A., Tachtsidis, I., Popescu, T., Thompson, J., Delazer, M., Zamarian, L., & Kadosh, R. C. (2013). Long-term enhancement of brain function and cognition using cognitive training and brain stimulation. Current Biology, 23(11), 987-992.
28.   Sumner, P. J., Bell, I. H., & Rossell, S. L. (2018). A systematic review of the structural neuroimaging correlates of thought disorder. Neuroscience & Biobehavioral Reviews, 84, 299-315.
29.   Tomaschek, K., Olechowski, A., Eppinger, S., & Joglekar, N. (2016). A Survey of Technology Readiness Level Users. Paper presented at the INCOSE International Symposium.
30.   Yoo, S.-S., Kim, H., Filandrianos, E., Taghados, S. J., & Park, S. (2013). Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains. PloS one, 8(4), e60410.
31.   Zulkarnain, A., Wahyuningtias, D., & Putranto, T. (2018). Analysis of IFE, EFE and QSPM matrix on business development strategy. Paper presented at the IOP Conference Series: Earth and Environmental Science.