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Fig. 7.3
Signal-processing steps to extract band-power features from raw EEG signals. The EEG
signal displayed here was recorded during
right hand
motor imagery (the instruction to perform
the imagination was provided at
t
= 0 s on the plots). The contralateral ERD during imagination is
here clearly visible. Indeed, the signal power in channel C3 (
left motor cortex
)in8
12 Hz clearly
-
decreases during this imagination of a right hand movement
BCI would exploit the spatial information by extracting features only from EEG
channels localized over the motor areas of the brain, typically channels C3 for right
hand movements, Cz for foot movements and C4 for left hand movements. It would
exploit the spectral information by focusing on frequency bands
μ
(8
12 Hz) and
β
-
(16
24 Hz). More precisely, for a BCI that can recognize left hand MI versus right
hand MI, the basic features extracted would be the average band power in 8
-
12 and
-
16
24 Hz from both channels C3 and C4. Therefore, the EEG signals would be
described by only four features.
There are many ways to compute band-power features from EEG signals
(Herman et al.
2008
; Brodu et al.
2011
). However, a simple, popular, and ef
-
cient
one is to
filter the EEG signal from a given channel into the fre-
quency band of interest, then to square the resulting signal to compute the signal
power, and
rst band-pass
finally to average it over time (e.g., over a time window of 1 s). This is
illustrated in Fig.
7.3
.
Unfortunately, this basic design is far from being optimal. Indeed, it uses only
two
fixed channels. As such, relevant information, measured by other channels
might be missing, and C3 and C4 may not be the best channels for the subject at
hand. Similarly, using the
24 Hz may not be
the optimal frequency bands for the current subject. In general, much better per-
formances are obtained when using subject-speci
fixed frequency bands 8
12 Hz and 16
-
-
c designs, with the best channels
and frequency bands optimized for this subject. Using more than two channels is
also known to lead to improved performances, since it enables to collect the rele-
vant information spread over the various EEG sensors.
7.3.2 Toward Advanced BCI Using Multiple EEG Channels
Both the need to use subject-specific channels and the need to use more than two
channels lead to the necessity to design BCI based on multiple channels. This is
con
rmed by various studies which suggested that, for motor imagery, eight
channels is a minimum to obtain reasonable performances (Sannelli et al.
2010
;
Arvaneh et al.
2011
), with optimal performances achieved with a much larger
