Brain–Computer Interfaces - Index of

The Graz Brain-Computer Interface 85
Fig. 5 Spontaneous EEG,
classification time courses for
ERD and ERS and timing of
the cue presentations during
brisk foot MI (from top to
bottom). TPs are marked with
an asterisk, FPs with a
triangle and FNs with a dash.
Modified from [53]
5
0
–5
1
0
1
0
EEG
^ ^ * – – *
ERD
* – * * *
ERS
0 5 10 15 20 25 30 35 s
both the SMA and the cortical foot representation areas. Considering the close proximity
of these cortical areas [16], and the fact that the response of the corresponding
networks in both areas may be synchronized, it is likely that a large-amplitude beta
rebound (beta ERS) occurs after foot MI. This beta rebound displays a high signalto-noise
ratio and is therefore especially suitable for detection and classification in
single EEG trials.
A group of able-bodied subjects performed cue-based brisk foot ME in intervals
of approximately 10 s. Detection and classification of the post-movement beta ERS
in unseen, single-trial, one-channel EEG signals recorded at Cz (Laplacian derivation;
compared to rest) revealed a classification accuracy of 74 % true positives
(TPs) and false positives (FPs) of 6 % [62]. This is a surprising result, because the
classification of the ERD during movement in the same data revealed a TP rate of
only21%.
Due to the similarity of neural structures involved in motor execution and in MI,
the beta rebound in both motor tasks displays similar features with slightly weaker
amplitudes in the imagery task [32]. We speculate, therefore, that a classifier set
up and trained with data obtained from an experiment with either cue-based foot
movement executions or motor imagery and applied to unseen EEG data obtained
from a foot MI task is suitable for an asynchronously operating “brain switch”.
First results of such an experiment are displayed in Fig. 5. The classification rate
of asynchronously performed post-imagery ERS classification (simulation of an
asynchronous BCI) was for TP 92.2 and FP 6.3 %.
4 Feature Extraction and Selection
In a BCI, the raw EEG signals recorded from the scalp can be preprocessed in several
ways to improve signal quality. Typical preprocessing steps involve filtering
in the frequency domain, reducing or eliminating artifacts such as EOG removal
or EMG detection, or the generation of new signal mixtures by suitable spatial