IEEE Computational Intelligence Magazine - May 2021 - 42

Shallow
ConvNet

Deep
ConvNet

RSTNN

MSNN
Block 1

EEGNet

MSNN
Block 2

MSNN
Block 3

Left
Hand
[a.u.]
1
Right
Hand
0
(a)
MSNN Block 1, Averaged f1SST

MSNN Block 2, Averaged f2SST

Class 1

MSNN Block 3, Averaged f3SST

Class 2

Class 3

Multi-Scale Spatio-SpectralSST
)
Temporal Feature, g (fconcat

Class 4

(b)
PSD + SVM

Shallow ConvNet

Deep ConvNet

RSTNN

0.81

0.18

0.01

0.80

0.20

0.01

0.73

0.26

0.01

0.74

0.24

0.01

Tired

0.10

0.84

0.05

0.15

0.80

0.05

0.15

0.80

0.05

0.15

0.80

0.05

Drowsy

0.06

0.13

0.81

0.05

0.14

0.82

0.08

0.17

0.75

0.07

0.16

0.77

Awake

Tired

Drowsy

Awake

Tired

Drowsy

Awake

Tired

Drowsy

Awake

Tired

Drowsy

MSNN

EEGNet

ESTCNN

1.0

Awake

0.73

0.25

0.02

0.71

0.26

0.03

0.81

0.18

0.01

Tired

0.15

0.80

0.05

0.16

0.78

0.05

0.13

0.83

0.04

0.8
0.6
0.4

Drowsy

0.08

0.16

0.75

0.14

0.18

0.68

0.04

0.11

0.85

Awake

Tired

Drowsy

Awake

Tired

Drowsy

Awake

Tired

Drowsy

0.2
0.0

The Ratio of Predictions

Awake

(c)
FIGURE 5 Investigation of learned weights (Fig. 5(a)) and represented features (Fig. 5(b)), and inspection of the practical usage of the proposed
network (Fig. 5(c) and 5(d)). (a) Topologically visualized activation pattern maps [30] of comparable baselines, and three spatial convolutions in
the proposed network. All these visualized patterns here are estimated by the first subject's first fold EEG signals in the GIST-MI dataset [40] and
normalized in a range between 0 and 1. Finally, [a.u.] denotes an arbitrary unit. Note that ESTCNN [26], Parallel CRN [17], and Cascade CRN have
no spatial convolutions, thus their patterns are not visualized. (b) Visualization of t-SNE transformed represented features for test SSVEP EEG
samples. The first three figures denote extracted features by the first, second, and final spatial convolutional layers of the proposed method. The
SST
final figure exhibits the GAP [39]-ed feature, G (f concat
) which is used for final decision making. (c) Normalized averaged confusion matrices estimated by comparable baselines and the proposed method using the SEED-VIG dataset [36]. (Continued)

42

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2021



IEEE Computational Intelligence Magazine - May 2021

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