Documents / Official release
This Defense Intelligence Reference Document was produced by the Defense Intelligence Agency's Defense Warning Office under the Advanced Aerospace Weapon System Applications program and dated 23 March 2010. It surveys noninvasive and invasive brain-machine interface technologies, including EEG, MEG, fMRI, NIRS and implanted electrode arrays, that could control external devices without limb-operated interfaces. It concludes that noninvasive electrical monitoring is the most promising near-term approach. For the long term it favors invasive single-neuron cortical connections.
From the source:Release of 2026-09-18 Incident: 3/23/10, Las Vegas, Nevada. Released with redactions. This document is a Defense Intelligence Reference Document (DIRD), a technical reference format used by the Defense Intelligence Agency (DIA) to capture baseline knowledge on a specific topic for later analytic use. DIRDs are best understood as reference and synthesis products rather than as original research. It is one of 38 DIRDs produced under the Advanced Aerospace Weapon System Applications Program (AAWSAP) between 2009 and 2011. Because AAWSAP’s scope permitted a broad range of supporting topics, not every DIRD in the series directly concerns aerospace systems or future threat assessment. The following summary reflects the DIRD’s scope and framing at the time of writing and should not be read as implying current validation of the concepts discussed. This DIRD surveys brain-machine interface technologies intended to allow users to control external devices without conventional manual controls, and it evaluates both noninvasive and invasive approaches for turning neural or related physiological signals into usable commands. The report reviews the underlying neural signals, distinguishes between open- and closed-loop control systems, and examines technologies including scalp-based electrical recording, magnetic and imaging-based methods, and implanted cortical interfaces, with particular attention to bandwidth, response time, signal quality, and practical usability. It concludes that, in the near term, the most practical systems are likely to be noninvasive electrical approaches that draw heavily on muscle and neural signals, while longer-term high-bandwidth control would likely require more advanced invasive interfaces capable of robust two-way communication with individual neurons. The document presents thought-based control of external devices as a research field with plausible assistive and specialized applications, while emphasizing that naturalistic, high-performance control remained constrained by major technical and physiological limits.
“Anderson”1 page
UNCLASSIFIED/ /FOA OFFIEIAk Uili 0'11k¥ 16 Cummings ML, Guerlain S. Developing operator capacity estimates for supervisory control of autonomous vehicles . Hum Factors 2007 Feb;49(1):1 - 15. 17 Enzinger C, Ropele S, Fazekas F, Loitfelder M, Gorani F, Seifert T, et al. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp Brain Res 2008 Sep ; 190(2) :215-23. 18 Berger H. Uber das elektrenkephalogramm des menschen . Archiv fur Psychiatrie und Nervenkrankheiten 1929;87(1) :527-80. 19 Davis P. Effects of acoustic stimuli on the waking human brain. Journal of Neurophysiology 1939;2:494-9. 20 Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G. The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. Ne uroimage 2007 Aug 15;37(2):539-50. 21 Kubler A, Kotchoubey B, Kaiser J, Wolpaw JR, Birbaumer N. Brain-computer communication: unlocking the locked in. Psycho! Bull 2001 May;127(3) :358-75. 22 Bai O, Lin P, Vorbach S, Floeter MK, Hattori N, Hallett M. A high performance sensorimotor beta rhythm -based brain-computer interface associated with human natura l motor behavior. J Neural Eng 2008 Mar;5(1):24 -35. 23 Iversen I, Ghanayim N, Kubler A, Neumann N, Birbaumer N, Kaiser J. Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology , Behav Brain Funct 2008;4:53. 24 Iversen IH, Ghanayim N, Kubler A, Neumann N, Birbaumer N, Kaiser J. A bra in-computer interface tool to assess cognitive functions in completely para lyzed patients with amyotrophic lateral sclerosis. Clin Neurophysiol 2008 Oct; 119(10):2214-23. 25 Sellers EW, Kubler A, Donchin E. Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory : the P300 Speller. IEEE Trans Neural Syst Rehabil Eng 2006 Jun; 14(2): 221 -4. 26 Greene K. Brain sensor for market research : A startup claims to read people's minds while they view ads. 2007 [May 12, 2009); Available from: http ://www .technologyreview .com/Biztech/19833/?a=f. 27 Greene K. Connecting Your Brain to the Game Using an EEG cap, a startup hopes to change the way people interact with video games. 2007 [May 12, 2009]; Available from: http ://www .technoloqyreview .com/Biztech/ 18276/? a = f. 28 Popescu F, Fazli S, Badower Y, Bla nkertz B, Mul ler KR. Single trial classification of motor imagination using 6 dry EEG electrodes. PLoS ONE 2007;2(7) :e637 . 29 van Gerven M, Jensen 0. Attention modulations of posterior alpha as a control signa l for two-dimensional brain computer interfaces. J Neurosci Methods 2009 Apr 30;179(1) :78-84. 30 Mellinger J, Schalk G, Braun C, Preiss! H, Rosenstiel W, Birbaumer N, et al. An MEG-based brain-computer interface (BCI) . Neuroimage 2007 Jul 1;36(3) :581 -93. 31 Ohta H, Matsui T, Uchikawa Y. Whole-head SQUID system in a superconducting magnetic shield. Neural Clin Neurophysiol 2004;2004:58 . 32 Kraus RH, Jr., Volegov P, Matlachov A, Espy M. Toward direct neural current imaging by resonant mechanisms at ultra-low field. Neuroimage 2008 Jan 1;39(1) :310-7. 33 McDermott R, Lee S, ten Haken B, Trabesinger AH, Pines A, Clarke J. Microtesla MRI with a superconducting quantum interference device. Proc Natl Acad Sci US A 2004 May 25;101(21):7857-61. 34 Chen Y, Intes X, Tailor DR, Regatte RR, Ma H, Ntziachristos V, et al. Probing rat brain oxygenation with near infrared spectroscopy (NIRS) and magnetic resonance imaging (MRI). Adv Exp Med Biol 2003;510:199-204. 35 Luu S, Chau T. Decoding subjective preference from single-trial near-infrared spectroscopy signals. J Neural Eng 2009 Feb;6(1):016003 . 36 Loeb GE. Cochlear prosthetics. Annu Rev Neurosci 1990;13 :357-71. 37 Bai ley L. New cochlear implant cou ld improve hearing. University of Michigan News Service [serial on t he Internet]. 2006: Available from : http ://www . um ich .edu/news/index . html?Releases/2006/Feb06/r020606a . 38 Cheng YC, Brown RW, Chung YC, Duerk JL, Fuj ita H, Lewin JS, et al. Calculated RF electric field and temperature distributions in RF therma l ablation: comparison with gel experiments and liver imaging. J Magn Reson Imag ing 1998 Jan-Feb ;8(1) :70-6. 39 Kamitani Y, Tong F. Decoding the visual and subjective contents of the human brain . Nat Neurosci 2005 May;8(5): 679 -85. 4° Kamitani Y, Tong F. Decoding seen and attended motion directions from activity in the human visual cortex . Curr Bio l 2006 Jun 6;16(11):1096-102 . 41 Fagg AH , Hatsopoulos NG, de Lafuente V, Maxon KA, Nemati S, Rebesco JM, et al. Biomimetic brain machine interfaces for the control of movement. J Neurosci 2007 Oct 31;27(44 ): 11842-6. 42 Kim HK, Carmena JM, Biggs SJ, Hanson TL, Nicolelis MA, Srinivasan MA. The muscle activation method : an approach to impedance control of brain-machine interfaces through a muscu loskeletal model of t he arm. IEEE Trans Biomed Eng 2007 Aug;54(8) : 1520 -9. 43 Kawata M. Brain control led robots . HFSP J 2008 Jun;2(3) : 136-42. 44 Jackson A, Moritz CT, Mavoori J, Lucas TH, Fetz EE . The Neurochip BCI: towards a neural prosthesis for upper limb function. IEEE Trans Neura l Syst Rehabil Eng 2006 Jun;14(2): 187 -90. 45 Leuthardt EC, Miller KJ, Scha lk G, Rao RP, Ojemann JG. Electrocorticography-based brain computer interface- the Seattle experience. IEEE Trans Neural Syst Reha bi I Eng 2006 Jun; 14(2) : 194-8. 46 Diorio C, Mavoori J. Computer electronics meet animal brains . IEEE Computer 2003;36(1) :69 -75. 4 7 Parikh H, Marzullo TC, Kipke DR. Lower layers in the motor cortex are more effective targets for penetrating microelectrodes in cortical prostheses. J Neural Eng 2009 Apr;6(2):026004 . 29 UNCLASSIFIED/ f FOA QFFI&IAk WSE 8,.L'/
Not linked to a story yet.
Official release, from the pursue collection. The PDF is mirrored here; the original link is above. 36 pages are in the text index: search them above, or from the library's search.