Systems, Man & Cybernetics - October 2017 - 20

been p
- roposed for a- rtifact removal. One of the BSS
-techniques is CCA [13], which -separates the m-channel EEG
signals X ^ t h = 6x 1 ^ t h, x 2 ^ t h, f, x m ^ t h@T into m
- aximally
autocorrelated and mutually uncorrelated sources S ^ t h =
6s 1 ^ t h, s 2 ^ t h, f, s m ^ t h@T, assuming that the EEG signals are
a linear combination of the sources. The linear combination
of sources can be represented by the -m ixing system
X ^ t h = A $ S ^ t h , where A ! R m # m is the unknown mixing
matrix. The unknown source signals S ^ t h can be derived
by introducing the de-mixing matrix
W ! R mm such that W $ X ^ t h = St ^ t h, where
St ^ t h . S ^ t h . Ideally, W is the inverse of
the unknown mixing matrix A.
The goal of BSS-CCA is to find the
matrices w x = 6w x w x gw x @ and w y =
6w y w y gw y @ that maximize the correlation t between two ca nonica l va r iates U ^ t h = w Tx X ^ t h and V ^ t h = w Ty Y ^ t h
as follows:

Computational Intelligence
for EEG Signal Processing
Removal of Artifacts to Enhance Signal Quality
Figure 5(a) presents a flowchart of the artifact removal process used to enhance EEG signal quality. One can apply a
bandpass filter (1-50 Hz) to -eliminate high--frequency noise
and the dc drift. An artifact-free EEG can be -reconstructed
after removing the artifacts. Several BSS techniques [33] have

1

1

(a)

2

2

m

m

w Tx C xy w y
,
^ w C xx w x h^ w yT C yy w y h
(1)
max
t ^U, V h =
w ,w
x

(b)

(c)

Figure 4. Wearable and wireless EEG devices for convenient EEG

recording in operating environments. (a) and (b) A simulated driving
environment. (c) A real-world driving environment.

Raw EEG

Filtering

Artifact
Removal

T
x

y

where Y ^ t h = X ^t - 1 h is the instantly
delayed signals of the observed EEG signals, C xx and C yy are auto-covariance
matrices, and C xy is the cross-covariance
matrix. After calculating the partial derivative with respect to w x and w y, the

Signal
Reconstruction

ArtifactFree EEG

0

1
Time (s)
(b)

2

C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16

0.95
0.9
0.85

Channel

Autocorrelation Coefficient

FP1
FP2
F7
F3
FZ
F4
F8
C3
CZ
C4
P3
PZ
P4
O1
OZ
O2

CCA Component

Channel

(a)

0.8
0.75
0.7
0.65

0

1
Time (s)
(c)

2

1

4
8
12 16
CCA Component
(d)

FP1
FP2
F7
F3
FZ
F4
F8
C3
CZ
C4
P3
PZ
P4
O1
OZ
O2
0

1
Time (s)
(e)

2

Figure 5. Artifact-free EEG reconstruction. (a) A flowchart of artifact removal. (b) Raw EEG signals contaminated

with eye blinks and muscle noises. (c) CCA components ordered in terms of (d) autocorrelation coefficients.
(e) Artifact-free EEG signals.

20	

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Oc tob e r 2017



Table of Contents for the Digital Edition of Systems, Man & Cybernetics - October 2017

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