IEEE Systems, Man and Cybernetics Magazine - January 2022 - 20
CSP
The CSP is a supervised learning
method for increasing the binary
classification results in oscillatory
EEG BCIs. This algorithm finds
optimal spatial filters (which are
nothing but a superposition of
actual EEG channels) while ensuring
that the variance of a filtered
signal is amplified for one binary
class and decayed for the other. We
describe the OVR-CSP proposed to
adapt a conventional CSP from a
binary classification to M (M > 2)
classes. For each class m, the OVRCSP
finds a matrix
W RCL
#
*
m !
,
where L is the number of spatial filters to maximize the
variance of class m against the rest:
W
Here, mRu
()im
R!
im
1
*
m
= argmax
W
Tr(
Tr(
class m, and W*
W | u j W)
<
W RmW)
R
< u
.
jm
!
is the mean covariance matrix of trials in
m is the concatenation of the L eigenvectors
associated with the L largest eigenvalues of the matrix
RR .
each of the M classes to obtain
ln==<* X
n
given by F = .. H where Fi is given by log10
F
>
F
ML
1
,
WW W R
* ** CML
= [,... ]1M !
.
Then, one can extract a spatially transformed trial by
XW ,,.., .nN1
Further, the feature vector is
2
<
IEEE Systems, Man and Cybernetics Magazine - January 2022
Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - January 2022
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IEEE Systems, Man and Cybernetics Magazine - January 2022 - Cover3
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