Biomedical Engineering Reference
In-Depth Information
true, which means the variance of the spatial filtered signal can be a good feature for
classification purposes:
(
)
(
)
(
)
(
)
var
FX
⋅
>
var
FX
⋅
var
FX
⋅
>
var
FX
⋅
(8.17)
L
L
R
L
R
R
L
R
Alternatively, the band powers of
Y
L
and
Y
R
are more straightforward features.
As shown in Figure 8.7, a more prominent peak difference can be seen on the power
spectrum of the CSP-filtered signal than on the original power spectrum.
For the purpose of visualization, the columns of the inverse matrix of
F
L
and
F
R
can be mapped onto each EEG electrode to get a spatial pattern of CSP source distri-
bution. As shown in the right-hand panel of Figure 8.7, the spatial distribution of
Y
L
and
Y
R
resembles the ERD topomap, which shows a clear focus in the left- and
right-hand area over the sensorimotor cortex.
8.3.3 Online Three-Class SMR-Based BCI
8.3.3.1 BCI System Configuration
In this study, three states of motor imagery were employed to implement a
multiclass BCI. Considering the reliable spatial distributions of ERD/ERS in
sensorimotor cortex areas, imagination of body part movements including those of
the left hand, right hand, and foot were considered as mental tasks for generating
detectable brain patterns. We designed a straightforward online feedback paradigm,
where real-time visual feedback was provided to indicate the control result of three
6
6
0.2
C3
C4
Left
Left
Right
Right
0.1
4
4
0
2
2
−
0.1
0
0
−
0.2
0
0 0 030
0
0 0 030
40
40
CSP (left)
80
80
CSP (left)
CSP (right)
0.2
Left
Left
60
60
Right
Right
0.1
40
40
0
20
20
−
0.1
−
0.2
0
0 0 030
0
0 0 030
40
40
CSP (right)
(a)
(b)
Figure 8.7
CSP spatial filtering enhances the SMR power difference between left- and right-hand
motor imagery. (a) CSP spatial pattern of left- and right-hand imagery; and (b) the PSD of the tempo-
ral signal, with a solid line for left imagery and a dashed line for right imagery. Upper row: PSD of raw
EEG from electrodes C3 and C4; lower row: PSD of derived CSP temporal signal.
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