AAWSAP DIRD, Technological Approaches to Controlling External Devices, March 2010

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.

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48 Baker KB, Nyenhuis JA, Hrd licka G, Rezai AR, Tkach JA, Shellock FG . Neurostimulation systems: assessment of
magnetic field interactions associated with 1.5- and 3-Tesla MR systems. J Magn Reson Imaging 2005
Jan;21(1):72 -7.
49 Wise KO. Silicon microsystems for neuroscience and neu ral prostheses. IEEE Eng Med Biol Mag 2005 Sep
Oct;24(5) : 22-9 .
50 Martinez Santiesteban FM, Swanson SD, Noll DC, Anderson DJ. Magnetic resonance compatibility of multichannel
silicon microelectrode systems for neural recording and stimu lation: design criteria, tests, and recommendations.
IEEE Trans Biomed Eng 2006 Mar; 53(3) : 547-58.
51 Paralikar KJ, Neuberge r T, Matsui JT, Barber AJ, Webb A, Clement RS . Feasibility and safety of longitudinal
magnetic resonance imaging in a rodent model with intracortical microwire implants. J Neural Eng 2009 Apr
15;6(3) :34001.
52 Blakely T, Miller KJ, Rao RP, Holmes MD, Ojemann JG . Loca lization and classification of phonemes using high
spatial resolution electrocorticography (ECoG) grids. Conf Proc IEEE Eng Med Biol Soc 2008;2008:4964-7.
53 Schalk G, Miller KJ, Anderson NR, Wi lson JA, Smyth MD, Ojemann JG, et al. Two -dimensional movement control
using electrocorticographic signals in humans . J Neural Eng 2008 Mar;5(1):75-84.
54 Marquez-Chin C, Popovic MR, Cameron T, Lozano AM, Chen R. Control of a neuroprosthesis for grasping using
off-line classification of electrocorticographic signals: case study . Spinal Cord 2009 Apr 21.
55 Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, et al. Neuronal ensemble control of
prosthetic devices by a human with tetraplegia. Nature 2006 Jul 13;442(7099): 164-71.
56 Kuiken TA, Li G, Lock BA, Lipschutz RD, Miller LA, Stubblefield KA, et al. Targeted muscle reinnervation for real
time myoelectric control of multifunction artificial arms . JAMA 2009 Feb 11;301(6):619-28.
57 Kuiken T. Targeted re innervation for improved prosthetic function. Phys Med Rehabi l Clin N Am 2006
Feb; 17(1) : 1-13 .
58 Warwick K, Gasson M, Hutt B, Goodhew I, Kyberd P, Andrews B, et al. The application of implant technology for
cybernetic systems. Arch Neural 2003 Oct;60(10) : 1369-73.
59 Nagel G, Szellas T, Huhn W, Kateriya S, Adeishvili N, Berthold P, et al. Channe lrhodopsin-2, a directly light-gated
cation-selective membrane channel . Proc Natl Acad Sci US A 2003 Nov 25;100(24) : 13940-5.
60 Degenaar P, Grossman N, Memon MA, Burrone J, Dawson M, Drakakis E, et al. Optobionic vision--a new
genetical ly enhanced light on retinal prosthesis . J Neural Eng 2009 Jun;6(3):035007.
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