有賴於技術的進步，以及近10年間各國政府以及研究機構，紛紛設立腦科學相關計畫，推動神經科學的研究發展，在這期間大腦影像、神經科技的技術也取得長足的進步，也因此讓腦機介面 (Brain Computer Interface, BCI) 技術能有更進一步的發展。BCI技術屬於跨多領的技術，也屬於新興發展的技術，其中牽涉到神經科學、計算機科學、材料學等領域。
文獻計量學 (Bibliometrics) 作為衡量知識擴散的重要來源，能夠觀測特定知識在作者、機構、年代間的演變，學術文獻與專利文獻，有完整的資料庫，並且具有特定格式，對於分析時能夠提供之許多資訊。專利資料庫中之數據觀察BCI技術於實際產業之現況，但對於仍在發展的新興技術，通常要在科學研究有突破性的重大發現才會為技術帶來革命性突破，因此分析新興技術時學術與專利文獻都是良好的資料來源。
本研究利用學術文獻資料庫web of science及專利文獻資料庫GPSS分別獲取學術文獻以及專利文獻之資訊，經數據處理分析BCI技術的學術及專利年分、作者、發表國家等趨勢圖，藉以觀察BCI技術現階段發展之情況。再利用公開開放軟體Citespace，並以中心性分析、聚類分析以及突現性分析，觀察BCI學術文獻中特定作者、國家、機構目前所著重之研究重點以及BCI技術研究的演進過程。本研究特點為引入專利資料，將專利資料進行適當格式處理後，以Citespace針對專利摘要與專利範圍 (Claim) 進行中心性分析及突現性分析，以觀察BCI技術專利著重之申請重點以及技術演進過程。並且利用上述分析概繪製出BCI技術分支地圖以及發展BCI相關技術的企業之技術現況。
根據分析結果所示BCI技術目前使用的領域仍以醫療用途為主，如調節神經、神經刺激、癱瘓病人的溝通及復健等。但於教育、增加認知、娛樂等適用於健康人類的用途也有發展的趨勢。BCI技術有三個關鍵元件，感測訊號、訊號萃取以及輸出設備，BCI技術最為關鍵也最需要克服的口即是--感測訊號。現感測元件主要仍是以成本較低攜帶操作便利的EEG (Electroencephalography)為主，未來技術應會朝使用兩種以上的大腦或生理訊號為主，針對健康人群仍以非侵入式為主，而侵入型的植入式的電極也會走向微小化、輕量化、多通道的趨勢。萃取訊號則是有賴於神經科技的發展及計算機科學的發展，能夠提取代表性的訊號即是目前BCI技術之缺口。控制設備部分以穿戴式的裝置為發展趨勢，如耳機、頭戴、項鍊等適用於一般人群。未來的社會邁向老齡化、神經疾病增多以及心理問題增加的趨勢，BCI能夠針對神經給予調整以及輔助增強大腦的功能，是具有發展潛力的新興技術之一。

Because of the advancement of technology, and the brain science-related projects. It promote the research and development of neuroscience. So Brain-computer interface (BCI) technology can be further developed. BCI related to multiple fields, Inolves neuroscience, computer science, materials science and other fields.
Bibliometrics is an important source for measuring knowledge. It can observe the evolution of specific knowledge among authors, institutions, and times.The scholarly literature and patent both have a complete database and a specific format, which can be provided for analysis. We usually using patent to observe the technology in the actual industry, but for emerging technologies, like BCI . The technology still developing, it is usually necessary to make breakthroughs in scientific research to bring revolutionary to the technology. Therefore, Scholarly literature and patent are good sources when analyzing emerging technologies.
This research uses the scholarly literature database “web of science” and the patent database “GPSS” to obtain the data. First we processed the data to obatian the number of publications, year points, authors and organizations. These data is observe the current developpment situation and trend of BCI technology. Then we use the open sourse “Citespace” to centrality analysis, cluster analysis, and burst analysis. It can observe the BCI current research focus of specific ones. The special in this study, is using Citespace to analysis patent.The original patent data was processed in an appropriate format, and Citespace was used to conduct a centrality analysis and aburst analysis of patent’s abstracts and claims. In order to observe the focus of BCI technology patent application and the technological evolution process. Then use the above result toget a branches map of BCI technology and the current state of technology of companies that develop BCI-related technologies.
According to the analysis results, the BCI technology currently used in the field is still mainly for medical purposes, such as nerve regulation, nerve stimulation, communication and rehabilitation of paralyzed patients. There is also a trend of development for applications suitable for healthy humans, such as education, increasing cognition, and entertainment. BCI has three key components, sensing signals, signal extraction and output devices. The most important part is “sensing signals”. The current mainstream sensing components are still EEG, which is low-cost and convenient to carry and operate. In the future, the technology should use more than one signal. And, the non-invasive type is still the mainstay for healthy people. The invasive implanted electrode will also move towards the trend of miniaturization, light weight, and multi-channel. The extraction of signals depends on the development of neurotechnology and computer science. The ability to extract representative signals is the current gap in BCI technology. The development trend of control equipment is wearable devices, such as earphones, headsets, necklaces, etc., which are suitable for the general population. In the future, the society will be aging, neurological diseases and psychological problems will increase. BCI can adjust the nerves and assist in enhancing the function of the brain. It is one of the emerging technologies with development potential.

中文資料
[1]2021年科學與科技10大趨勢 重塑疫後新經濟與社會. (2021). https://iknow.stpi.narl.org.tw/Post/Read.aspx?PostID=17491
[2]中華民國創業投資商業同業公會. 創投創業投資之定義定義. http://www.tvca.org.tw/information
[3]中華創業育成協會. (2018). 漫談台灣創投事業與投資評估模式.
[4]今週刊. (2018). 創新是企業成長及永續經營的驅動力. https://www.businesstoday.com.tw/article/category/80393/post/201803050020/%E7%A0%94%E7%99%BC%E8%88%87%E5%89%B5%E6%96%B0%E6%98%AF%E4%BC%81%E6%A5%AD%E6%88%90%E9%95%B7%E5%8F%8A%E6%B0%B8%E7%BA%8C%E7%B6%93%E7%87%9F%E7%9A%84%E9%A9%85%E5%8B%95%E5%8A%9B
[5]天下雜誌. (2015). 新創企業為何活不下去？. https://www.cw.com.tw/article/5063950
[6]天下雜誌. (2019). 新創公司 5 年內存活率只有 1%？創業真的很危險嗎？. https://www.cw.com.tw/article/5095239?template=transformers
[7]方曙, 張嫻, & 肖國華. (2007). 專利情報分析方法及應用研究.
[8]吳憂. (2018). 新創公司必須懂！產品專利佈局的5個步驟. https://mag.addmaker.tw/2018/04/09/%e6%96%b0%e5%89%b5%e5%85%ac%e5%8f%b8%e5%bf%85%e9%a0%88%e6%87%82%ef%bc%81%e7%94%a2%e5%93%81%e5%b0%88%e5%88%a9%e4%bd%88%e5%b1%80%e7%9a%845%e5%80%8b%e6%ad%a5%e9%a9%9f/
[9]張曉林. (2018). 專利技術情報分析模型構建及其應用研究. 圖書館雜誌, 37(10), 78.
[10]李世暉. (2020). 日本斑馬企業起飛救經濟.
[11]李江. (2013). 基於引文的知識擴散研究評述. 情報資料工作, 34(4), 36-40.
[12]肖滬衛. (2003). Internet 專利競爭情報開發研究. 情報雜誌, 22(10), 52-54.
[13]具創新能力之新創事業認定原則.
[14]延智. (2007). 高科技產業分析. 五南圖書出版股份有限公司.
[15]林明宜, & 國家實驗研究院科技政策研究與資訊中心. (2020). 神經科技應用 創投搶進. https://iknow.stpi.narl.org.tw/Post/Read.aspx?PostID=16591
[16]查全率. (2012). https://terms.naer.edu.tw/detail/1678994/
[17]柳玲, & 馬宏珺. (2014). 給中小企業專利申請的幾點建議. 企業技術開發(9), 69-71.
[18]研究前沿報告. https://clarivate.com/zh-hant/products/scientific-and-academic-research/research-fronts/
[19]徐作聖. (2012a). 研發成果如何商品化. https://www.cw.com.tw/article/5035167
[20]徐作聖. (2012b). 研發成果如何商品化. https://www.cw.com.tw/article/5035167
[21]高越. (2020). 美國腦機介面技術研究及應用進展. 資訊通信技術與政策, 46(12), 75.
[22]張嘉彬. (2016). 從研究方法角度探討研究前沿. 大學圖書館, 20(1), 88-112.
[23]曹學偉, & 高曉巍. (2020). 技術預見主要研究方法綜述及可實施路徑分析. 今日科苑.
[24]陳建鈞. (2020). 馬斯克用三隻小豬演示Neuralink生化腦技術，披露腦機介面最新成果. https://www.bnext.com.tw/article/59046/neuralink-brain-computer-interface-2020-pig
[25]陳柏中. (2016). 腦機介面趨勢發展分析. https://portal.stpi.narl.org.tw/index/article/10227
[26]曾彥菁. (2019). 影響未來十年的2020十大科技趨勢，除了AI與區塊鏈，還有哪些？. https://futurecity.cw.com.tw/article/1078
[27]黃柏璋. (2017). 歐盟將擬訂關鍵促成技術（Key Enabling Technologies）促進總策略. https://stli.iii.org.tw/article-detail.aspx?no=64&tp=1&d=3204
[28]黃慕萱, & 何蕙菩. (2007). 圖書資訊學知識來源與知識擴散學科之研究. 圖書資訊學刊, 5(1&2), 1-30.
[29]經濟部工業局. (2019). 2019 生技醫藥白皮書.
[30]資誠聯合會計師事務所. (2019). 2019 台灣新創生態圈大調查. https://www.pwc.tw/zh/publications/download/download-2019-taiwan-startup-ecosystem-survey.html
[31]劉致均. (2015). 左右為難-你需要多準則決策分析! https://highscope.ch.ntu.edu.tw/wordpress/?p=65645
[32]蕭鬱蓉. (2019). 歐盟科研與創新政策發展經驗與對我國之啟示. 國家發展委員會 經濟研究, 19.
[33]賴英崑. (2020). TRL簡介. https://iace.org.tw/file/downloadFile.action;jsessionid=41B65566187161A568AF6DAD42869C15?folderConfigKey=newsAttachFolder&downloadFileSubPath=news_20200320143523_f9650f45-1ef5-4ac7-896e-4e1aed2e9145_01+TRL%E7%B0%A1%E4%BB%8B.pdf
[34]賴荃賢. (2018). 漫談台灣創投事業與投資評估模式.
[35]識別 Web of Science 中的研究前沿：從指標到意義. https://clarivate.com/zh-hant/campaigns/identifying-research-fronts-in-web-of-science-from-metrics-to-meaning/
外文資料
[1]5 Top Brain-Computer Interface Startups Impacting Engineering. https://www.startus-insights.com/innovators-guide/5-top-brain-computer-interface-startups-impacting-engineering/
[2]Aarikka-Stenroos, L., Sandberg, B., & Lehtimäki, T. (2014). Networks for the commercialization of innovations: A review of how divergent network actors contribute. Industrial Marketing Management, 43(3), 365-381.
[3]administration, U. S. s. B. Startups &High growth business EB/OL. https://www.sba.gov/category/types-businesses/high-growth-high-tech
[4]Arora, A., Belenzon, S., & Patacconi, A. (2017). Papers to patents. Nature, 552(7683).
[5]Arora, A., Belenzon, S., & Patacconi, A. (2018). The decline of science in corporate R&D. Strategic Management Journal, 39(1), 3-32.
[6]Avilov, O. O., Popov, A. O., Timofieiev, V. I., Bougrain, L., & Henaff, P. Estimation of Imaginary Movements Quality Based on Machine Learning for Brain Computer Interface Applications.
[7]Aydin, E. A., Bay, O. F., & Guler, I. (2017). P300-based asynchronous brain computer interface for environmental control system. IEEE journal of biomedical and health informatics, 22(3), 653-663.
[8]Azagra-Caro, J. M., Barberá-Tomás, D., Edwards-Schachter, M., & Tur, E. M. (2017). Dynamic interactions between university-industry knowledge transfer channels: A case study of the most highly cited academic patent. Research policy, 46(2), 463-474.
[9]Basberg, B. L. (1987). Patents and the measurement of technological change: a survey of the literature. Research policy, 16(2-4), 131-141.
[10]Bashashati, A., Ward, R. K., & Birch, G. E. (2007). Towards development of a 3-state self-paced brain-computer interface. Computational intelligence and neuroscience, 2007.
[11]Battke, B., Schmidt, T. S., Stollenwerk, S., & Hoffmann, V. H. (2016). Internal or external spillovers—Which kind of knowledge is more likely to flow within or across technologies. Research policy, 45(1), 27-41.
[12]Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological forecasting and social change, 73(7), 835-844.
[13]Blankertz, B., Acqualagna, L., Dähne, S., Haufe, S., Schultze-Kraft, M., Sturm, I., Ušćumlic, M., Wenzel, M. A., Curio, G., & Müller, K.-R. (2016). The Berlin brain-computer interface: progress beyond communication and control. Frontiers in Neuroscience, 10, 530.
[14]Boyack, K. W., & Klavans, R. (2010). Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American society for information science and technology, 61(12), 2389-2404.
[15]Brain-Computer Interfaces: Principles and Practice. (2011). Oxford University Press.
[16]Brunner, C., Birbaumer, N., Blankertz, B., Guger, C., Kübler, A., Mattia, D., Millán, J. d. R., Miralles, F., Nijholt, A., Opisso, E., Ramsey, N., Salomon, P., & Müller-Putz, G. R. (2015, 2015/01/02). BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-computer interfaces, 2(1), 1-10. https://doi.org/10.1080/2326263X.2015.1008956
[17]Caldwell, D. J., Ojemann, J. G., & Rao, R. P. (2019). Direct electrical stimulation in electrocorticographic brain–computer interfaces: Enabling technologies for input to cortex. Frontiers in Neuroscience, 13, 804.
[18]Capogrosso, M., Milekovic, T., Borton, D., Wagner, F., Moraud, E. M., Mignardot, J.-B., Buse, N., Gandar, J., Barraud, Q., & Xing, D. (2016). A brain–spine interface alleviating gait deficits after spinal cord injury in primates. Nature, 539(7628), 284-288.
[19]Castells, M. (2004). The network society A cross-cultural perspective. Edward Elgar.
[20]Cattan, G., Mendoza, C., Andreev, A., & Congedo, M. (2018). Recommendations for integrating a P300-based brain computer interface in virtual reality environments for gaming. Computers, 7(2), 34.
[21]cbinsights. (2019a). 21 Neurotech Startups To Watch: Brain-Machine Interfaces, Implantables, And Neuroprosthetics. https://www.cbinsights.com/research/neurotech-startups-to-watch/
[22]cbinsights. (2019b). The Top 20 Reasons Startups Fail. https://www.cbinsights.com/research/startup-failure-reasonstop/
[23]Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American society for information science and technology, 57(3), 359-377.
[24]Chesbrough, H., Vanhaverbeke, W., & West, J. (2006). Open innovation: Researching a new paradigm. Oxford University Press on Demand.
[25]Cognixion. https://www.cognixion.com/
[26]Coyle, S. M., Ward, T. E., & Markham, C. M. (2007). Brain–computer interface using a simplified functional near-infrared spectroscopy system. Journal of neural engineering, 4(3), 219.
[27]Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological forecasting and social change, 73(8), 981-1012.
[28]Djamal, E. C., & Lodaya, P. (2017). EEG based emotion monitoring using wavelet and learning vector quantization. 2017 4th international conference on Electrical Engineering, Computer Science and Informatics (EECSI),
[29]EEG Headsets and the Rise of Passthoughts. (2017). http://neurosky.com/2017/02/EEG-headsets-and-the-rise-of-passthoughts/
[30]Enkel, E., & Gassmann, O. (2007). Driving open innovation in the front end.
[31]Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology, 70(6), 510-523.
[32]Garfield, E. (1955). Citation indexes for science. Science, 122(3159), 108-111.
[33]Gnoli, C., & Hjørland, B. (2020). Science mapping. https://www.isko.org/cyclo/science_mapping#2.1
[34]Gomez-Gil, J. (2012). Brain Computer Interfaces, a Review. Sensors, 1211-1279.
[35]Granstrand, O. (1998). Towards a theory of the technology-based firm. Research policy, 27(5), 465-489.
[36]Hill, N. J., Gupta, D., Brunner, P., Gunduz, A., Adamo, M. A., Ritaccio, A., & Schalk, G. (2012). Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. JoVE (Journal of Visualized Experiments)(64), e3993.
[37]Hollanders, H. (2020). European Innovation Scoreboard 2020: Methodology Report. Report of the European Innovation Scoreboard project Brussels: European Commission.
[38]Hong, K.-S., & Khan, M. J. (2017). Hybrid brain–computer interface techniques for improved classification accuracy and increased number of commands: a review. Frontiers in neurorobotics, 11, 35.
[39]Huang, D., Qian, K., Fei, D.-Y., Jia, W., Chen, X., & Bai, O. (2012). Electroencephalography (EEG)-based brain–computer interface (BCI): A 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(3), 379-388.
[40]Johnson, N., Carey, J., Edelman, B., Doud, A., Grande, A., Lakshminarayan, K., & He, B. (2018). Combined rTMS and virtual reality brain–computer interface training for motor recovery after stroke. Journal of neural engineering, 15(1), 016009.
[41]Jun, S., Park, S. S., & Jang, D. S. (2012). Technology forecasting using matrix map and patent clustering. Industrial Management & Data Systems.
[42]Kayal, A. (1999). Measuring the pace of technological progress: implications for technological forecasting. Technological forecasting and social change, 60(3), 237-245.
[43]Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American documentation, 14(1), 10-25.
[44]Kleinberg, J. (2006). Temporal dynamics of on-line information streams. In Data Stream Management: Processing High-Speed Data,
[45]Kothe, C. A., & Makeig, S. (2013). BCILAB: a platform for brain–computer interface development. Journal of neural engineering, 10(5), 056014.
[46]Lim, K. (2000). The relationship between publications and patents by researchers at five companies. In Sloan Working Paper (Vol. 4120). Massachusetts Institute of Technology.
[47]Liu, H.-h., Yang, G.-l., Liu, X.-x., & Song, Y.-y. (2020). R&D performance assessment of industrial enterprises in China: a two-stage DEA approach. Socio-Economic Planning Sciences, 71, 100753.
[48]Liu, S.-J., & Shyu, J. (1997). Strategic planning for technology development with patent analysis. International journal of technology management, 13(5-6), 661-680.
[49]Lowe, D. (2019). Startups And Their Publications. https://blogs.sciencemag.org/pipeline/archives/2019/07/30/startups-and-their-publications
[50]Mansfield, E. (1991). Academic research and industrial innovation. Research policy, 20(1), 1-12.
[51]Mansfield, E. (1998). Academic research and industrial innovation: An update of empirical findings. Research policy, 26(7-8), 773-776.
[52]Markman, G. D., Siegel, D. S., & Wright, M. (2008). Research and technology commercialization. Journal of Management Studies, 45(8), 1401-1423.
[53]Marshakova, I. (1973). Document coupling system based on references taken from Science Citation Index. Russia, Nauchno-Teknicheskaya Informatsiya, Ser, 2.
[54]Martin, C., Samans, R., Leurent, H., Betti, F., Drzeniek-Hanouz, M., Geiger, T., KEARNEY, F. A. T., Aurik, J., Zuazua, M., Schulz, O., & Blaylock, A. (2018). Readiness for the Future of Production Report 2018.
[55]Martino, J. P. (2003). A review of selected recent advances in technological forecasting. Technological forecasting and social change, 70(8), 719-733.
[56]Meo, S., & Usmani, A. (2014). Impact of R&D expenditures on research publications, patents and high-tech exports among European countries. European Review for Medical and Pharmacological Sciences, 18(1), 1-9.
[57]Meyer-Krahmer, F., & Schmoch, U. (1998). Science-based technologies: university–industry interactions in four fields. Research policy, 27(8), 835-851.
[58]Min, B.-K., Marzelli, M. J., & Yoo, S.-S. (2010). Neuroimaging-based approaches in the brain–computer interface. Trends in biotechnology, 28(11), 552-560.
[59]monitor, G. e. (2020). GEM 2019 / 2020 GLOBAL REPORT. https://www.gemconsortium.org/report/gem-2019-2020-global-report
[60]Moore, J. F. (1993). Predators and prey: a new ecology of competition. Harvard business review, 71(3), 75-86.
[61]Morris, S. A., Yen, G., Wu, Z., & Asnake, B. (2003). Time line visualization of research fronts. Journal of the American society for information science and technology, 54(5), 413-422.
[62]Moutinho, P., Fontes, M., & Godinho, M. (2007). Do individual factors matter? A survey of scientists’ patenting in Portuguese public research organisations. Scientometrics, 70(2), 355-377.
[63]NASA. (2012). Technology Readiness Level. https://www.nasa.gov/directorates/heo/scan/engineering/technology/technology_readiness_level
[64]neurable. https://neurable.com/
[65]neuralink. https://neuralink.com/
[66]neurotechedu. http://learn.neurotechedu.com/preprocessing/
[67]Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors, 12(2), 1211-1279.
[68]OECD. (2013). OECD Science, Technology and Industry Scoreboard 2013. https://doi.org/doi:https://doi.org/10.1787/sti_scoreboard-2013-en
[69]Osareh, F. (1996). Bibliometrics, citation analysis and co-citation analysis: A review of literature I.
[70]Ouellette, L. L. (2017). Who reads patents? Nature biotechnology, 35(5), 421-424.
[71]Panetta, K. (2019). Gartner Top 10 Strategic Technology Trends for 2020. https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/
[72]Pavitt, K. (1984). Sectoral patterns of technical change: towards a taxonomy and a theory. Research policy, 13(6), 343-373.
[73]Persson, O. (1994). The intellectual base and research fronts of JASIS 1986–1990. Journal of the American Society for information Science, 45(1), 31-38.
[74]Petrovich, E. (2020). Science mapping. https://www.isko.org/cyclo/science_mapping
[75]Pfurtscheller, G., Allison, B., Brunner, C., Bauernfeind, G., Solis-Escalante, T., Scherer, R., Zander, T., Mueller-Putz, G., Neuper, C., & Birbaumer, N. (2010). The hybrid BCI Front. Neurosci, 4, 42.
[76]Pfurtscheller, G., Allison, B. Z., Bauernfeind, G., Brunner, C., Solis Escalante, T., Scherer, R., Zander, T. O., Mueller-Putz, G., Neuper, C., & Birbaumer, N. (2010). The hybrid BCI. Frontiers in Neuroscience, 4, 3.
[77]Pfurtscheller, G., Neuper, C., Guger, C., Harkam, W., Ramoser, H., Schlogl, A., Obermaier, B., & Pregenzer, M. (2000). Current trends in Graz brain-computer interface (BCI) research. IEEE transactions on rehabilitation engineering, 8(2), 216-219.
[78]Placido, F., Valeriani, D., Lintott, R., Orozco, H., Chuang, Y. M., Rabipour, S., Coleman, P., Armentrout, K., Meldazy, G., Ruiz-Blondet, M. V., Siebert, B., Hough, M., Zappullo, C., & Vizcaya, D. neurotechedu Preprocessing. http://learn.neurotechedu.com/preprocessing/#interpolation
[79]Rashid, M., Sulaiman, N., PP Abdul Majeed, A., Musa, R. M., Bari, B. S., & Khatun, S. (2020). Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review. Frontiers in neurorobotics, 14, 25.
[80]Schumpeter, J. A. (2016). 49. Capitalism, Socialism and Democracy. Columbia University Press.
[81]Shane, S. (2001). Technology regimes and new firm formation. Management science, 47(9), 1173-1190.
[82]Shyu, K.-K., Lee, P.-L., Lee, M.-H., Lin, M.-H., Lai, R.-J., & Chiu, Y.-J. (2010). Development of a low-cost FPGA-based SSVEP BCI multimedia control system. IEEE Transactions on biomedical circuits and systems, 4(2), 125-132.
[83]Small, H. (1973). Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science, 24(4), 265-269.
[84]Small, H. G. (1977). A co-citation model of a scientific specialty: A longitudinal study of collagen research. Social studies of science, 7(2), 139-166.
[85]Stegman, P., Crawford, C. S., Andujar, M., Nijholt, A., & Gilbert, J. E. (2020). Brain–computer interface software: A review and discussion. IEEE Transactions on Human-Machine Systems, 50(2), 101-115.
[86]Takahashi, M., Takeda, K., Otaka, Y., Osu, R., Hanakawa, T., Gouko, M., & Ito, K. (2012). Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study. Journal of neuroengineering and rehabilitation, 9(1), 1-6.
[87]Talke, K., & Hultink, E. J. (2010). Managing diffusion barriers when launching new products. Journal of Product Innovation Management, 27(4), 537-553.
[88]Tang, H., Yan, R., & Tan, K. C. (2017). Cognitive navigation by neuro-inspired localization, mapping, and episodic memory. IEEE Transactions on Cognitive and Developmental Systems, 10(3), 751-761.
[89]Tarango, J., & Machin-Mastromatteo, J. D. (2017). The Role of Information Professionals in the Knowledge Economy: Skills, Profile and a Model for Supporting Scientific Production and Communication. Chandos Publishing.
[90]Union, E. (2014). Roadmap for cross-cutting KETs activities in Horizon 2020. https://ec.europa.eu/growth/sites/default/files/docs/body/cross-cutting-kets-roadmap-brochure_en.pdf
[91]Van de Velde, E. (2015). Key Enabling Technologies (KETs) Observatory: Methodology Report. IDEA Consult.
[92]Van Erp, J., Lotte, F., & Tangermann, M. (2012). Brain-computer interfaces: beyond medical applications. Computer, 45(4), 26-34.
[93]Venthur, B. (2015). Design and implementation of a brain computer interface system.
[94]Vertechy, R., Frisoli, A., Dettori, A., Solazzi, M., & Bergamasco, M. (2009). Development of a new exoskeleton for upper limb rehabilitation. 2009 IEEE International Conference on Rehabilitation Robotics,
[95]Wei, Y., Wu, Y., & Tudor, J. (2017). A real-time wearable emotion detection headband based on EEG measurement. Sensors and Actuators A: Physical, 263, 614-621.
[96]Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical neurophysiology, 113(6), 767-791.
[97]Wolpaw, J. R., & McFarland, D. J. (1994). Multichannel EEG-based brain-computer communication. Electroencephalography and clinical Neurophysiology, 90(6), 444-449.
[98]Yokoi, H. (2009). Cyborg (brain–machine/computer interface). Advanced Robotics, 23(11), 1451-1454.