Strategic analysis of neurotechnologies in Iran

Document Type : Research

Authors

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

Abstract

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.

Keywords

Main Subjects

References

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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.
Journal of Strategic Management Studies
Volume 11, Issue 42
July 2020
Pages 19-35

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History

  • Receive Date: 16 January 2020
  • Revise Date: 09 April 2020
  • Accept Date: 03 July 2020

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