Brain–Computer Interfaces - Index of

Neurofeedback Training for BCI Control 69
detecting some types of brain signals that do not require neurofeedback training
(see e.g. [38] and chapter “Brain–Computer Interface in Neurorehabilitation” in this
book).
BCIs often rely on motor imagery. Users imagine movements of different
body parts, such as the left hand, right hand, or foot [25]; for a review see
[37]). This method has been shown to be particularly useful for mental control
of neuroprostheses (for a review, see [13]). There is strong evidence that motor
imagery activates cortical areas similar to those activated by the execution of the
same movement [25]. Consequently, EEG electrodes are placed over primary sensorimotor
areas. Characteristic ERD/ERS patterns are associated with different
types of motor imagery (see chapter “Dynamics of Sensorimotor Oscillations in
a Motor Task” for details) and are also detectable in single trials using an online
system.
3.1 Training with the Graz-BCI
Before most “motor imagery” BCIs can be efficiently used, users have to undergo
training to obtain some control of their brain signals. Users can then produce brain
signals that are easier to detect, and hence a BCI can more accurately classify different
brain states. Prior to starting online feedback sessions with an individual, his/her
existing brain patterns (e.g. related to different types of motor imagery) must be
known. To this end, in the first session of the Graz-BCI standard protocol, users
must repeatedly imagine different kinds of movement (e.g., hand, feet or tongue
movement) in a cue-based mode while their EEG is recorded. Optimally, this would
entail a full-head recording of their EEG, with topographical and time-frequency
analyses of ERD/ERS patterns, and classification of the individual’s brain activity
in different imagery conditions. By applying, for example, the distinction sensitive
learning vector quantization (DSLVQ) [39] to the screening data, the frequency
components that discriminate between conditions may be identified for each participant.
Classification accuracy can also be calculated. This shows which mental states
may be distinguished, as well as the best electrode locations. Importantly, specific
individualized EEG patterns may be used in subsequent training sessions, where the
user receives on-line feedback of motor imagery related changes in the EEG.
In a typical BCI paradigm, feedback about performance is provided by (i) a continuous
feedback signal (e.g., cursor movement) and (ii) by the outcome of the trial
(i.e. discrete feedback about success or failure). Noteworthily, BCI control in trained
subjects is not dependent on the sensory input provided by the feedback signal. For
example, McFarland et al. [6] reported that well-trained subjects still displayed EEG
control when feedback (cursor movement) was removed for some time. Further,
this study showed that visual feedback can not only facilitate, but also impair EEG
control, and that this effect varies across individuals. This highlights the need for displays
that are immersive, informative, and engaging without distracting or annoying
the user.