IEEE Systems, Man and Cybernetics Magazine - January 2020 - 19

r can finally be obtained. It is notable that W
r shall be
W
saved. For the test, testing data directly use the mixed,
space-filtering matrix obtained from set training for filr is used to finally filter
tering. The mixed space filter W
the EEG data, E i, in a single experiment and obtain X i
of 36 # T :
r i.
X i = WE

styles, which involves 16 convolutional layers and five
pooling layers. For the extraction of content features, a
CNN of five layers is used for convolution. Mean pooling is conducted after each convolutional layer, and a
content feature matrix is finally generated. As for the
extraction of style features, first, all of the feature
maps at a certain layer are processed after being put in
the network. There is large quantity of feature maps at
each layer, and, accordingly, the inner product of each
pair of the feature maps and the style feature matrix
that contains the texture and color information of the
maps are obtained. After the content and style features
are extracted, artistic paintings are created. Content
and style features as well as white noise images are
input into the VGG19 network. The gradient descent
method is used to solve the total loss function, i.e., the
minimum value of (6). The output result of the white
noise image is used to update x constantly and uses
the VGG19 network to rectify the result. To decrease
the total loss, a painting based on a creation of the artist can finally be obtained:

(4)

As a result, the features of signal X i are extracted after
space filtering. First, the variances of the row vectors of
X i are obtained. Because of the variance among single
EEG signals, the difference among some of the eigenvalues
is large; therefore, the logarithm of variance is used to alleviate the difference among data, as displayed in (5):
v i = log (var (X i)),

(5)

where v i is the eigenvector of a single experiment E i .
There are six eigen elements in total; in other words,
each sample contains six eigenvalues. Then, long
short-term memory (LSTM) is used to train and build
a classifier.

y , ay , x
y ) = aL content ( p
y , xy ) + bL style (ay , xy ),
L total ( p
Artwork Contents and Style-Processing
Model Based on VGG-19
The processing of artworks consists of extracting its
particular creation style. The works in the same class
are extended, the contents of the works are extracted,
and artworks are created. The VGG19 network in [15] is
used to extract and rebuild the features of contents and

Input Data

(6)

y , xy ) is the loss of contents, L style (ay , x
y ) is the
where L content ( p
loss of style, and a, b is the factor of influence.
Attention-Based RNN Emotion Analysis Model
In this article, we use an attention-based RNN model to
evaluate a human's emotion, and, the higher of the

Style Classification
With Motion
Imaging

EEG
History
Artworks

Style Match
+

Real-Time
Content

Real-Time
Content

VGG-19
Pool 1

Pool 2

Emotion
Analysis

Pool 3 Pool 4
Pool 5
+
Output

Conv 1

Conv 2

Conv 3

Conv 4

Conv 5

fc6 fc7

Emotion
Data

Color
Correction

Artificial
Artworks

Attention-Based
RNN

Figure 2. The CreativeBioMan algorithm flowchart. conv: convolution; fc: full connection.

Ja nu a r y 2020

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

19



IEEE Systems, Man and Cybernetics Magazine - January 2020

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