repeatedly and reliably maintain the
specified correlations between brain
signals and intent. Figure 3 outlines
three different conceptualizations of
where adaptation might take place to
establish and maintain good BCI performance: In the first, the BCI adapts
to the user; in the second, the user
adapts to the BCI; and, in the third,
user and system adapt to each other.
A number of BCI systems are designed to detect user performance of
specific cognitive tasks. Curran et al. 3
suggested that cognitive tasks (such
as navigation and auditory imagery)
might be more useful in driving a BCI
than motor imagery. However, sensorimotor rhythm-based BCIs may provide several advantages over systems
that depend on complex cognitive operations; for example, the structures
involved in auditory imagery are also
likely to be driven by auditory sensory input. Wolpaw and McFarland37
reported that with extended practice
users report motor imagery is no lon-
figure 4. BCI2000 design consists of four modules: operator, source, signal processing,
Operator deals with system configuration and online presentation of results to the
investigator; during operation, information is communicated from source to signal processing
to user application and back to source (adapted from schalk et al. 25).
figure 5. hardware in the Wadsworth Center’s home BCI system, including 16-channel
electrode cap for signal recording, solid-state amplifier, laptop, and additional monitor
as user display.
ger necessary to operate a sensorimotor rhythm-based BCI. As is typical
of many simple motor tasks, performance becomes automatized through
extended practice. Automatized performance may be less likely to interfere with mental operations users
might wish to engage in concurrent
with their BCI use; for example, composing a manuscript is much easier
if the writer does not need to think
extensively about each individual keystroke.
As noted, EEG recording may be
contaminated by non-brain activity
(such as line noise and muscle activity); see Fatourechi et al. 8 for a review.
Activity recorded from the scalp represents the superposition of many
signals, some originating in the brain,
some elsewhere. These signals include
potentials generated by retinal dipoles,
or eye movement and blinks, and facial
muscles. It is noteworthy that mental
effort is often associated with changes
in eye-blink rate and muscle activity. 35
BCI users might generate these artifacts without being aware of what they
are doing simply by making facial expressions associated with effort.
Facial muscles can generate signals with energy in the spectral bands
used as features in an SMR-based
BCI18 Muscle activity can also modulate SMR activity; for example, users
can move their right hands in order
to desynchronize the mu rhythm over
the left hemisphere. This sort of mediation of the EEG through peripheral
muscle movements was a concern in
the early days of BCI development.
As noted earlier, Dewan6 trained users to send Morse code messages using occipital alpha rhythms modulated by voluntary movements of eye
muscles. For this reason, Vaughan
et al. 33 recorded EMG from 10 distal
limb muscles, while BCI users used
central mu or beta rhythms to move
a cursor to targets on a video screen.
EMG activity was very low in these
well-trained users. Most important,
the correlations between target position and EEG activity could not be
accounted for through EMG activity.
Similar studies have been done with
BCI users moving a cursor in two dimensions, 37 showing that SMR modulation does not require actual movements or muscle activity.