Brain–Computer Interfaces - Index of

282 C. Guger and G. Edlinger
Fig. 1 Components that influence BCI development
For higher numbers of channels, the real-time capabilities of the BCI system must
be also guaranteed for accurately timed generation of the desired control signals for
feedback to e.g. external devices.
On the subject side, it is very important that the BCI system adapts itself to the
learning process of the user. This can either be done in on-line mode, where the
classification algorithm is retrained after each decision, or in a quasi on-line mode,
where e.g. 40 decisions are made and the classifier is then retrained to adapt to the
new control signals [4]. However, it is also important that the subject adapts to the
BCI system and learns how to control the system. Hence, the provided feedback
strategy is very important for a successful learning process. Immediate feedback of
a cursor movement, fast control in computer games or correct responses of a spelling
device are therefore vital requirements. Very important for a subject’s success in BCI
control is the choice of the optimal BCI approach which can be based on: (i) oscillations
in the alpha and beta range, (ii) slow waves, (iii) P300 components or (iv)
steady-state visual evoked potentials (SSVEP) [5–8]. For some subjects, oscillations
in the alpha and beta band during motor imagery work better than the evoked potential
approach and vice versa. Therefore, it is very important that the BCI system
enables testing the subject with all these different approaches to find the optimal one.
A traditional development approach is shown in Fig. 2, left side. Here, feature
extraction algorithms (e.g. recursive least square (RLS) estimation of adaptive
autoregressive parameters – AAR) and classification algorithms (e.g. linear discriminant
analysis – LDA) are typically implemented in a first step for the off-line
analysis of already acquired EEG data. After adaptation and optimization of the
algorithms, the hardware for the EEG acquisition and the real-time processing environment
is developed, leading to a new implementation process of the off-line
algorithms and to many iterations between the algorithm and hardware design. On
the other side, the rapid prototyping process combines the algorithm design with the
real-time tests on the hardware (see Fig. 2, right side). Typically, an already proven
hardware and software environment is used for such an approach. The diagrams in