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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An issue develops when an entirely new device is considered. The form of the
movement control algorithm is similar to a population vector in that movement at each
time step is determined by a vector sum of the neurons' normalized firing rates
multiplied by a set of linear coefficients. Modern neuroscience has proven that tens of
thousands, even up to millions of neurons fire in concert to perform even the most
mundane of movements. These extended neural pathways developed through full
duplex closed loop training. It will thus be quite an issue in an open-loop design to
develop an algorithm that can decode control signals for a system the brain has never
controlled before, say a 12-direction rocket stabilization system. 17 This places another
limitation on the open-loop model, even using complex nonlinear fitting, and has driven
development of the closed-loop model.
CLOSED-LOOP PERIPHERAL ARRAYS UTILIZING VISUAL FEEDBACK
In a closed- loop system that provides feedback to a control system, the differences
between the two models of movement become apparent. Sensory feedback within the
system is used for error correction of fine motor control, such as balance - without fine
motor control and error correction for balance, the human body could not stand up. In
the model with abstract cortical control, feedback corrections are processed externally
to the BMI and sensory feedback to the brain is usually limited to observation. This is a
subtle distinction between open-loop with visual feedback and closed-loop abstract
control that includes visual feedback. In the former, the visua l feedback is intended to
show success of a control command being sent by the brain, whereas in the latter,
visual feedback is showing success of supervisory function of a semiautonomous
mechanical device.
eural Recording
JAPAN
Figure 4. Experimental Overview of Brain-Controlled Robot in a Closed-Loop With Visual Feedback
Experiment. After decod ing walking-related Information from a monkey's brain activity while walking on a
treadmill, these data were relayed from Duke University in USA to the Advan ced Telecommunication Research in
17 Twelve directions are the minimum number of positive controls commands to +/- x, y, and z thrust, as well as
increasi ng or decreasing ro ll, pitch , and yaw. These are six degrees of freedom but as far as a BMI control system
is concerned, increasing x and decreasing x are separate commands to decode.
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