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.

UNCLASSIFIED/ /FOR: 8Ffl@IJltt t:191!! er•t I
of a spiking train to trigger a switch, or as complex as decoding signal from noise
utilizing a 300-channel EEG cap.
~-------------------------,
: Additional sensory :
I ---1 -------------------'
~
Brain
Output
interface
,----1 Input
interface
Output
signal
processing
Components of a
Closed-loop BMI
Control System
Input
signal
processing
External
Device
Figure 2. The General Layout of a Closed-Loop Control Interface. The input and output interfaces can attach
to read or affect activity of cerebra l or peripheral neurons. Add itional sensory feedback channels such as visual
displays, audio tones, or haptic feedback are possible . In some system designs, especially noninvasive types, these
sensory feedback channels replace stimulation inputs. In an open-loop interface, either the input or output signal
paths are absent.
One consideration in developing a BMI control system is the utility of the application:
does it make sense to directly tie these controls to neural activity? The bulk of research
in direct neural interfaces is toward the goal of restoring mechanical capabilities to
those individuals who have either lost limbs or lost control of limbs through central
nervous system injury or disease. In these cases it is of obvious utility to produce a
system that moves a cursor across screen to select an item over the course of several
seconds; however, for a healthy individual, it is far more practical to execute a hand
movement to control a mechanical device4 to achieve the same goal.
An exception to consider is the case of applications where the subject's hands or feet
are already occupied with other essential tasks in the overall application. A research
question for any such application is whether training a subject to uti lize a BMI is
advantageous over a more complex mechanical interface. Furthermore, whether
training on a new BMI affects learning/retaining proficiency with similar BMis is an
additional concern.
Moreover, for any BMI, one can quantitatively describe the information transfer in
controlling the magnitude of human motor action (Reference 12). Since the advent of
digital control of external devices, this information is expressed in equivalent bits per
4 Mechanical device as used here represents any limb-operated interface, such as a mouse or touchscreen.
UNCLASSIFIED/ /FOR OFFI&IAk W&E 8Ptk¥
5