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Declarative Consciousness for Reconstruction

Carmena, J.M.; 2013b. Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces. Cornell Uniersity Library Kaplan, A.; Lebedev, M.; and Berger, T. 2013. Roundtable on Life Extension of the Brain in a Full Body Prosthesis with Biological Blood Substitutes and Brain Computer Interfaces with Optional Neuroprostheses. In Proceedings of The Global Futures 2045 International Congress, GF2045, 14, Abstracts. New York. 2045 Strategic Social Initiative Koene, R. 2012. Mind Transfer: Human Brains in Different

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EEG Feature Selection for BCI Based on Motor Imaginary Task

G.H., Wrappers for feature subset selection , Artificial Intelligence , 1-2 , 1997, 273-324. [5] Lakany H., Conway B. A., Understanding intention of movement from electroencephalograms, Expert Systems , 24 , 5, 2007, 295-304. [6] Lebedev M.A., Nicolelis M.A.L., Brain-machine interfaces: past, present and future. Trends in Neurosciences , 29 , 9, 2006, 536-546. [7] Leocani L., Toro C., Zhuang P., Gerloff C., Hallet M., Event-related desynchronization in reaction time paradigms: a comparison with event

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Editorial: Whole Brain Emulation seeks to Implement a Mind and its General Intelligence through System Identification

Solution for Chronic Brain-Machine Interfaces. arXiv:1307.2196. Wang, P. 2011. Artificial General Intelligence: A Gentle Introduction. Retrieved December 31, 2013, from

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Assessment and Testing of WPT System for the Design of Modern Wireless Energy Transmission Solutions

6. REFERENCES [1] Wu, R.; Li, W.; Luo, H.; Sin, J.K.; Yue, C.P. Design and characterization of wireless power links for brain–machine interface applications. IEEE Trans. Power Electron. 2014, 29, 5462–5471. [2] RamRakhyani, A.K.; Mirabbasi, S.; Chiao, M. Design and optimization of resonance-based efficient wireless power delivery systems for biomedical implants. IEEE Trans. Biomed. Circuits Syst. 2011, 5, 48–63. [3] Liu, C.; Chau, K.T.; Zhang, Z.; Qiu, C.; Li, W.; Ching, T. Wireless power transfer and fault diagnosis of high-voltage power line

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The Prospects of Whole Brain Emulation within the next Half- Century

:// Seo, D., Carmena, J. M., Rabaey, J. M., Alon, E., Maharbiz, M. M. 2013. Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces. ArXiv:1307.2196 Shelley, M. 1818. Frankenstein. London: Lackington, Hughes, Harding, Mavor & Jones. The Sixth Day. 2000. Spottiswoode, Roger. Columbia Pictures. Film. Trafton, A. 2012. Robots that reveal the Inner Workings of Brain Cells. MIT News. Available electronically at

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Early-Stage Pilot Study on Using Fractional-Order Calculus-Based Filtering for the Purpose of Analysis of Electroencephalography Signals

– Comparison of the Two Novel Methods. In Proceedings 19th Conference on Methods and Models in Automation in Robotics (MMAR) 2014 , (pp. 804–807). DOI: 10.1109/MMAR.2014.6957459 Kline, A., & Jaydip, D. (2015). Noninvasive Brain-Machine Interface to Control Both Mecha TE Robotic Hands Using Emotiv EEG Neuroheadset. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering , 9(4), 323–327. Magin, R. L. (2006). Fractional Calculus in Bioengineering . Begell House. Oustaloup, A., Levron, F., Mathieu, B., & Nanon, F

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Operationalising Responsible Research and Innovation – tools for enterprises

Introduction Technological progress and radical innovation carry promise of a higher life quality but at the same time are inseparably connected with risks and uncertainties. Many inventions also raise critical ethical issues. Genetically modified organisms (including food), vaccinations (especially for children), shale gas drilling, gene editing, mass surveillance, nanotechnologies, robotics, brain-machine interface – these are just examples of controversial topics where hopes and fears collide in society. Supporters and opponents of particular scientific

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