directional changes in brain rhythms;
for example, users were required to
maintain an increase in the size of an
EEG rhythm for minutes at a time. In
a series of subsequent studies, this
group showed that the signals controlling the cursor were actual EEG activity
and that covert muscle activity did not
mediate this EEG control. 18, 31
These initial SMR results were subsequently replicated by others21, 24 and
extended to multidimensional control. 37 These P300 and SMR BCI studies together showed that noninvasive
EEG recording of brain signals can
serve as the basis for communication-and-control devices.
A number of laboratories have explored the possibility of developing
BCIs using single-neuron activity detected by microelectrodes implanted
in the cortex12, 30 (see Figure 2c). Much
of the related research has been done
in non-human primates, though trials
have also been done with humans. 12
Other studies have shown that recordings of electrocorticographic (ECoG)
activity from the surface of the brain
can also provide signals for a BCI15;
to date they indicate that invasive recording methods can also serve as the
basis for BCIs. Meanwhile, important
issues concerning their suitability for
long-term human use have yet to be
resolved.
Earlier studies demonstrating operant conditioning of single units in the
motor cortex of primates, 9 hippocampal theta rhythm of dogs, 2 and sensorimotor rhythm in humans29 showed
brain activity could be trained with
operant techniques. However, these
studies were not demonstrations of
BCI systems for communication and
control since they required subjects
to increase brain activity for periods
of many minutes, showing that brain
activity could be tonically altered in
a single direction through training.
However, communication-and-control
devices require that users be able to
select from at least two distinct alternatives; that is, there must be at least
one bit of information per selection.
Effective communication-and-control
devices require users to rapidly switch
between multiple alternatives.
In addition to electrophysiological
measures, researchers have also dem-
onstrated the feasibility of magneto-
encephalography (MEG), 20 functional
magnetic resonance imaging (fMRI), 28
and near-infrared systems (fNIR). 4
Current technology for recording
MEG and fMRI is both expensive and
bulky, making it unlikely for practical
applications in the near term; fNIR is
potentially cheaper and more com-
pact. However, both fMRI and fNIR are
based on changes in cerebral blood
flow, an inherently slow response. 11
Electrophysiological features repre-
sent the most practical signals for BCI
applications today.
system Design
Communication-and-control applica-
tions are interactive processes requir-
ing users observe the results of their
effort to maintain good performance
and correct mistakes. For this reason,
BCIs must run in real time and provide
real-time feedback to users. While
many early BCI studies satisfied this
requirement, 24, 38 later studies were
often based on offline analyses of pre-
recorded data1; for example, the Lotte
et al. 16 review of studies evaluating BCI
signal-classification algorithms found
most used offline analyses. Indeed,
the current popularity of BCI research
is probably due in part to the ease of-
fline analyses are performed on pub-
licly available data sets. While such
offline studies may help guide actual
online BCI investigations, there is no
guarantee that offline results will gen-
eralize to online performance. Users’
brain signals are often affected by BCI
outputs that are in turn determined
by the algorithm the BCI is using. It
is thus not possible to predict results
precisely from offline analyses that
cannot account for these effects.
figure 3. three approaches to BCI design.
let the Machines Run
Operant Conditioning
Optimized Co-Adaptation
user
user
user
BCI system
BCI system
BCI system
Arrows indicate the element that adapts; the BCI, the user, or both adapt
to optimize and maintain BCI performance (adapted from McFarland et al. 17).
