Brain–Computer Interfaces - Index of

The First Commercial Brain–Computer Interface Environment 295
DATA ACQUISITION
STORAGE
VISUALIZATION
FEATURE
EXTRACTION
CLASSIFICATION
PARADIGM
Fig. 11 Simulink model for the real-time feature extraction, classification and paradigm
presentation
multiplies the features with the WV. The “Paradigm” block is responsible for the
presentation of the experimental paradigm in this case the control of the arrows on
the screen and the feedback.
In addition to the parameter estimation and classification algorithms, spatial filters
such as common spatial patterns (CSP), independent component analysis (ICA),
or Laplacian derivation can also be applied [2]. In this case, spatial patterns are also
calculated from the first session to filter the EEG data before feature extraction and
classification. This also means the subject specific WV is trained on this specific
spatial filter. During the real-time experiments with feedback, the EEG data is influenced
and changed because of the BCI system feedback and the subject has the
chance to learn and adapt to the BCI system. However, it is necessary to retrain the
BCI system based on the new EEG data. Important is that both the spatial filter and
the classifier are calculated based on feedback data if they are used for feedback
sessions. As illustrated in Fig. 10, several iterations are necessary to allow both systems
to adapt. The subject that performed the experiment described in Fig. 10 was
the first subject ever who reached 100% accuracy in 160 trials [2] of BCI control.
3.2 Training with a P300 Spelling Device
A P300 spelling device can be based on a 6 × 6 matrix of different characters displayed
on a computer screen. The row/column speller flashes a whole row or a whole
column of characters at once in a random order as shown in Fig. 12. The single
character speller flashes only one single character at an instant in time. This yields