Systems, Man & Cybernetics - January 2015 - 7

goal-directed behavior is dynamically modulated by perceptual feedback resulting from executed actions.
Brain signals for a BMI can be recorded from single
Intention
Brain Recordings
Estimation
neurons using microelectrode arrays implanted in the
(Perceptual)
Congnitive
brain (single-unit activity) or as the concerted activity
States
of neuronal populations of different sizes depending on
the position of the electrodes-either implanted in the
brain (local field potential), on the surface of the brain
(electrocorticography), or outside the scalp (electroencephalography). These approaches provide complementary advantages, and a combination of technologies may
Multimodal
be necessary to achieve the ultimate goal of controlling
Feedback
neuroprostheses capable of replicating any kind of body
movement as easily as able-bodied people control their
Shared Control
natural limbs [8].
No matter the origin of the brain signals, current BMIs
Execution
partly emulate human motor control as they decode cortical correlates of movement parameters-from the onset
of a movement to directions to instantaneous velocity-to
generate the sequence of movements for the neuroprosFigure 1. A BMI loop: a BMI transforms brain activthesis. However, a closer look shows that motor control
ity (recorded at the micro-, meso-, or macrolevel) into
results from the combined activity of the cerebral cortex,
actions by decoding the user's intention. The estimated
intention is enlarged with contextual information (extersubcortical areas, and spinal cord. In fact, many elements
nal input plus the internal state of the neuroprosthesis)
of skilled movements, from manipulation to walking, are
using shared control. The execution of actions conveys
mainly handled at the brain stem and spinal cord level,
rich multimodal feedback to the user, who makes perwith cortical areas providing an abstraction of the desired
ceptual cognitive decisions that dynamically modulate
movement. This hierarchical organization supports the
his or her goal-directed behavior.
hypothesis that complex behaviors can be controlled using the
cantly improve the control of the
low-dimensional output of a BMI
prosthesis by allowing the user to
in conjunction with intelligent deA brain-machine
feel the environment in cases in
vices in charge to perform low-level
which natural sensory afferents
commands, akin to the role of the
interface is about
are compromised-either through
subcortical and spinal cord levels
transforming neural
other senses or by stimulating the
in human motor control.
activity into action
body or even the brain directly to
Our brain-controlled wheelchair
recover the lost sensation. Fur(Figure 2) illustrates the future of
and sensation into
thermore, rich multimodal feedintelligent neuroprostheses that,
perception.
back is essential to promote the
like our spinal cord and musculouser's agency and owernship of
skeletal system, work in tandem
the prosthesis.
with motor commands decoded
Finally, we can decode and infrom the user's brain cortex [9]. Ustegrate the prosthetic control-loop information about perers can drive it reliably and safely over long periods of time
ceptual cognitive processes of the user that are crucial for
thanks to the incorporation of shared-control (or contextvolitional interaction, such as awareness to errors made
awareness) techniques. This relieves users from the need
by the device [12], anticipation of critical decision points,
to continuously deliver all the necessary low-level control
and lapses of attention. As in natural motor control, this
parameters and, therefore, reduces their cognitive workload
information is associated with processing feedback and
and facilitates split attention [10].
should dynamically modulate interaction.
A further component that will facilitate intuitive and
natural control of motor neuroprosthetics is the incorpoAbout the Author
ration of rich multimodal feedback and neural correlates
José del R. Millán (jose.millan@epfl.ch) is the Defitech
of perceptual processes resulting from this feedback. ReProfessor at the École Polytechnique Fédérale de Laualistic sensory feedback must convey artificial tactile and
sanne, Switzerland, where he explores the use of brain
proprioceptive information-i.e., the awareness of the posignals for multimodal interaction and, in particular, the
sition and movement-of the neuroprosthesis [11]. This
development of noninvasive brain-controlled robots and
type of sensory information has the potential to signifiJa nu a r y 2015

IEEE SySTEMS, MAn, & CyBErnETICS MAgAzInE

7



Table of Contents for the Digital Edition of Systems, Man & Cybernetics - January 2015

Systems, Man & Cybernetics - January 2015 - Cover1
Systems, Man & Cybernetics - January 2015 - Cover2
Systems, Man & Cybernetics - January 2015 - 1
Systems, Man & Cybernetics - January 2015 - 2
Systems, Man & Cybernetics - January 2015 - 3
Systems, Man & Cybernetics - January 2015 - 4
Systems, Man & Cybernetics - January 2015 - 5
Systems, Man & Cybernetics - January 2015 - 6
Systems, Man & Cybernetics - January 2015 - 7
Systems, Man & Cybernetics - January 2015 - 8
Systems, Man & Cybernetics - January 2015 - 9
Systems, Man & Cybernetics - January 2015 - 10
Systems, Man & Cybernetics - January 2015 - 11
Systems, Man & Cybernetics - January 2015 - 12
Systems, Man & Cybernetics - January 2015 - 13
Systems, Man & Cybernetics - January 2015 - 14
Systems, Man & Cybernetics - January 2015 - 15
Systems, Man & Cybernetics - January 2015 - 16
Systems, Man & Cybernetics - January 2015 - 17
Systems, Man & Cybernetics - January 2015 - 18
Systems, Man & Cybernetics - January 2015 - 19
Systems, Man & Cybernetics - January 2015 - 20
Systems, Man & Cybernetics - January 2015 - 21
Systems, Man & Cybernetics - January 2015 - 22
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Systems, Man & Cybernetics - January 2015 - 28
Systems, Man & Cybernetics - January 2015 - 29
Systems, Man & Cybernetics - January 2015 - 30
Systems, Man & Cybernetics - January 2015 - 31
Systems, Man & Cybernetics - January 2015 - 32
Systems, Man & Cybernetics - January 2015 - 33
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Systems, Man & Cybernetics - January 2015 - 35
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Systems, Man & Cybernetics - January 2015 - 37
Systems, Man & Cybernetics - January 2015 - 38
Systems, Man & Cybernetics - January 2015 - 39
Systems, Man & Cybernetics - January 2015 - 40
Systems, Man & Cybernetics - January 2015 - 41
Systems, Man & Cybernetics - January 2015 - 42
Systems, Man & Cybernetics - January 2015 - 43
Systems, Man & Cybernetics - January 2015 - 44
Systems, Man & Cybernetics - January 2015 - 45
Systems, Man & Cybernetics - January 2015 - 46
Systems, Man & Cybernetics - January 2015 - 47
Systems, Man & Cybernetics - January 2015 - 48
Systems, Man & Cybernetics - January 2015 - Cover3
Systems, Man & Cybernetics - January 2015 - Cover4
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