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

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second transferred. Higher bit rates equate to more efficient interfaces. Baseline
examples of performance include finger pointing, which can convey 14 bits/s of
information (Reference 13), operation of a mouse about 8 bits/s (Reference 14), while
stylus tapping a soft QWERTY keyboard on a PDA has a lower rate, around 5 bits/s
(Reference 15).
Once these questions are researched and answered for a given application, the design
choice should be made whether a new mechanical interface, or an optical interface that
tracking detailed motion of the eyes, body and limbs, may be the optimum solution
rather than a BMI. 5
Another consideration in developing a BMI control system is the cognitive limit of
supervisory control of external devices. The current state of the art for human
interfaces involves volition of control, meaning that there is some executive
supervision, typically involving the pre-frontal cortex, active during the control process.
Recent studies relating to control of semiautonomous vehicles show that there are
significant limits on the number of supervisory activities that can be undertaken
simultaneously (Reference 16). In response to these limits, one can consider utilization
of nonsupervised brain networks in control of external devices, similar to those brain
systems that initiate very complex muscle coordination activity such as martial arts or
playing a musical instrument, or systems like those that are responsible for autonomic
function. Application from such research is long term, but there are current studies that
show BMI training of at least one spinal cord patient exhibited differential activity in the
cerebellum, owing to extraordinary plasticity (Reference 17). It is therefore not out of
the realm of possibility that sufficient input and output interfaces that include neural
pathways through the cerebellum could result in over-learning a BMI application to the
point that supervisory control is developed and thus significant cognitive effort is not
required.
Noninvasive Technologies
There are four noninvasive technologies currently in widespread use in monitoring brain
activity. The two direct-measure technologies involve detecting surface currents from
the relative voltage changes at or just below the scalp, electroencephalography (EEG),
and detection of near scalp magnetic fields associated with neural pathway current flow,
magnetoencephalography (MEG). The remaining two indirect measures involve
monitoring BOLD response through rapid successions of whole brain MRI (functional
MRI or fMRI), and near-infrared spectroscopy (NIRS). 6
The primary technology used for BMI is also the oldest. EEG was first described in 1929
(Reference 18) and now exists in several derivative forms. Traditional EEG uses
electrodes at the surface of the scalp to measure and amplify potential differences
between points above the cortical surface and a fixed reference such as the average
reading from the earlobes. Traditional EEG data is analyzed by breaking up the power
spectrum into several bands between 0.5 and 100 Hz. A derivative form of EEG
developed about 10 years later is called event-related potentials, or ERP. In ERP, scalp
5 There is a valid argument to be made that optical tracking and analysis, especially of the eyes, is a BMI
technology as defined in the current treatise. It is excluded as one merely by fiat.
6 NIRS is occasionally referred to as functional NIRS or fNIRS. NIRS using multiple sources to produce 3D images of
internal changes in blood flow is occasionally called diffuse optical tomography (DOT). DOT is a more general
technology that can also refer methods such as to visible-light laser excitation of tissue.
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