IEEE Systems, Man and Cybernetics Magazine - July 2020 - 35

FBTSC obtained the best accuracy for subject-specific calCalibration Type [F(1,22) = 33.308, p # 0.0001 ], but not for
ibration (both about 61%), while FBCSP obtained the best
Calibration Type*Algorithm [GG(1,22) = 0.558, p = 0.618].
performances for the subject-independent one (55.2%).
In posthoc analyses, a student t-test for paired samples
with Bonferroni adjustments showed no significant differences between algorithms in the subject-specific or subArousal
ject-independent studies. However, per for mances
The balanced classification accuracies for the emotion-
obtained suggested better (but insignificantly so) results
arousal data set are reported in Figure 4. We then perwith the CNN compared to other algorithms, in both
formed a two-way ANOVA with repeated measures, with
factor Algorithms and Calibration
-subject-specific and subject-indeType. Results revealed significant
pendent studies. RGCs, particueffects for Algorithms [GG(1,32) =
larly the newly proposed ones
Obtaining reasonable
9.177, p # 0.0001 ], Calibration Type
(FBfgMDM and FBTSC), provided
the second-best performances, just
[F(1,32) = 4.262, p = 0.048], and
performances in a
after the CNN. On the other hand,
Algorithms*Calibration Type
subject-independent
the baseline CSP + LDA obtained
[GG(1,32) = 3.894, p = 0.008].
the worst results.
In posthoc analyses, a student
calibration from
t-test for paired samples with Bonferonly 2 s of EEG data
roni corrections showed significant
Valence
and
21
users
for
differences with the subject-specific
The balanced classification accuracalibration between CNN and all
cies obtained are reported in Figcalibration makes
other classifiers (see the results in
ure 3. We ran a two-way ANOVA for
the CNN particularly
the supplementary material). No
repeated measures to evaluate the
algorithm showed better results
impact of Algorithm on the emotion-
interesting.
than others with subject-indepenvalence data set, regarding the Calident calibration. Overall, the best
bration Type. The results showed
results were all obtained by RGCs,
significant differences in Algorithm
FBFgMDM, and FBTSC for the subject-specific calibration
[GG(1,32) = 6.918, p = 0.002], Calibration Type [F(1,32) =
and FgMDM for the subject-independent one.
21.732, p¡0.0001], and Calibration Type*Algorithm [GG(1,32) =
5.374, p = 0.003].
Discussion, Conclusion, and Future Work
In posthoc analyses, a student t-test for paired samples
In this article, we explored promising classification algowith Bonferroni corrections showed a significant differrithms, both existing and new, to classify mental workload
ence between FBTSC and CNN for subject-specific calibraand emotions (valence and arousal) from EEG signals,
tion [perf FBTSC = 61.09%, perfCNN = 46.32%; p # 0.05 ]. No
with both subject-specific and subject-independent calialgorithm showed better results than others with the subbration. Altogether, we studied CSP + LDA, FBCSP + LDA,
ject-independent calibration. Overall, FBFgMDM and

100

100

80

80

Accuracy (%)

70
60
50

40
20

40

Algorithms

Subject Specific

Subject Specific

Subject Independent

Figure 2. The classification accuracy of each

algorithm on the workload data set.

	

N
N
C

C
TS

M

Algorithms

FB

C
FB
F

gM
D

TS

M
M
D

SP

Fg

FB
C

N
N
C

SC

M

FB
T

C
FB

Fg
M
D

TS

SP
Fg
M
D
M

C
FB

SP

0

30

C
SP

60

C

Accuracy (%)

90

Subject Independent

Figure 3. The balanced classification accuracy on

the emotion-valence data set.

Ju ly 2020

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE	

35



IEEE Systems, Man and Cybernetics Magazine - July 2020

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