Systems, Man & Cybernetics - October 2017 - 24
Feedback
EEG
EOG
Classifier 1
m1
Classifier 2
m2
..
.
..
.
Classifier j
mj
..
.
..
.
Classifier n
mn
D-S Theory
Fusion
Final
Decision
Figure 8. The multimodal fusion of EEG and EOG signals for a hybrid BCI using the D-S theory.
where all " A, B, C , 3 2 H, A ! Q, and m 1 5 m 2 ^Q h = 0. The
conflict coefficient k = | B + C = A m 1 ^ B h m 2 ^C h measures
the degree of conflict between m 1 and m 2 . A larger value
of k indicates greater conflict between two sources.
The efficacy of the D-S theory in multiaspect data
fusion is demonstrated in a typical BCI application,
namely, detecting whether the cognitive state of participants is alert or not during a realistic sustained-attention driving task [43]. The distracted driving experiment
consists of an unexpected deviation (swerving) of the
car and the presentation of mathematical equations. A
flowchart of the proposed system is shown in Figure 8.
The simultaneously recorded EEG and electro-oculography (EOG) signals were used to build an ensemble of
support vector machines (SVMs) and naïve Baye's classifiers (NBCs).
Table 1 provides the classification results of different
comparative models reported by five-fold cross-validation. The average accuracies of the NBC and SVM using
EEG signals alone are 55.7 ! 5.1% and 70.9.3 ! 3.8%,
respectively. The average accuracies of the NBC and
SVM using EOG signals alone are 59.6 ! 4.8% and 60.3 !
2.1%, respectively. When using the D-S theory to fuse the
outcomes derived from distinct classifiers, the classification accuracy can reach an average value of 75.1 !
3.6%. These results suggest that multimodality information with D-S theory fusion can effectively enhance the
performance of BCIs.
Conclusion
This article presents the latest BCI-related research
done in our group. Our previous work applied computational intelligence technology in BCIs (i.e., drowsy and
distracted driving applications [44]) to inspire detailed
investigations of practical issues in real-life applications.
Novel EEG devices featuring dry electrodes facilitate
and speed up electrode positioning before recording and
allow subjects to move freely in operational environments. We also demonstrate the feasibility of applying
CCA, RBFNs, effective connectivity measurements, and
D-S theory to help BCIs extract informative knowledge from brain signals. Two
recent trends in research in the compuTable 1. The classification accuracies obtained using
individual classifiers and enhanced by D-S fusion.
tational and artificial intelligence community, big data and deep learning, are
Modality
EEG
EOG
Fusion
expected to impact the direction and
development of BCIs. Those ongoing
Classifier
NBC
SVM
NBC
SVM
D-S Theory
studies will enable the next generation
Mean ! SD (%) 55.7 ! 5.1 70.9 ! 3.8 59.6 ! 4.8 60.3 ! 2.1
75.1 ! 3.6
of BCIs.
24
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Oc tob e r 2017
Table of Contents for the Digital Edition of Systems, Man & Cybernetics - October 2017
Systems, Man & Cybernetics - October 2017 - Cover1
Systems, Man & Cybernetics - October 2017 - Cover2
Systems, Man & Cybernetics - October 2017 - 1
Systems, Man & Cybernetics - October 2017 - 2
Systems, Man & Cybernetics - October 2017 - 3
Systems, Man & Cybernetics - October 2017 - 4
Systems, Man & Cybernetics - October 2017 - 5
Systems, Man & Cybernetics - October 2017 - 6
Systems, Man & Cybernetics - October 2017 - 7
Systems, Man & Cybernetics - October 2017 - 8
Systems, Man & Cybernetics - October 2017 - 9
Systems, Man & Cybernetics - October 2017 - 10
Systems, Man & Cybernetics - October 2017 - 11
Systems, Man & Cybernetics - October 2017 - 12
Systems, Man & Cybernetics - October 2017 - 13
Systems, Man & Cybernetics - October 2017 - 14
Systems, Man & Cybernetics - October 2017 - 15
Systems, Man & Cybernetics - October 2017 - 16
Systems, Man & Cybernetics - October 2017 - 17
Systems, Man & Cybernetics - October 2017 - 18
Systems, Man & Cybernetics - October 2017 - 19
Systems, Man & Cybernetics - October 2017 - 20
Systems, Man & Cybernetics - October 2017 - 21
Systems, Man & Cybernetics - October 2017 - 22
Systems, Man & Cybernetics - October 2017 - 23
Systems, Man & Cybernetics - October 2017 - 24
Systems, Man & Cybernetics - October 2017 - 25
Systems, Man & Cybernetics - October 2017 - 26
Systems, Man & Cybernetics - October 2017 - 27
Systems, Man & Cybernetics - October 2017 - 28
Systems, Man & Cybernetics - October 2017 - 29
Systems, Man & Cybernetics - October 2017 - 30
Systems, Man & Cybernetics - October 2017 - 31
Systems, Man & Cybernetics - October 2017 - 32
Systems, Man & Cybernetics - October 2017 - 33
Systems, Man & Cybernetics - October 2017 - 34
Systems, Man & Cybernetics - October 2017 - 35
Systems, Man & Cybernetics - October 2017 - 36
Systems, Man & Cybernetics - October 2017 - 37
Systems, Man & Cybernetics - October 2017 - 38
Systems, Man & Cybernetics - October 2017 - 39
Systems, Man & Cybernetics - October 2017 - 40
Systems, Man & Cybernetics - October 2017 - 41
Systems, Man & Cybernetics - October 2017 - 42
Systems, Man & Cybernetics - October 2017 - 43
Systems, Man & Cybernetics - October 2017 - 44
Systems, Man & Cybernetics - October 2017 - Cover3
Systems, Man & Cybernetics - October 2017 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/smc_202310
https://www.nxtbook.com/nxtbooks/ieee/smc_202307
https://www.nxtbook.com/nxtbooks/ieee/smc_202304
https://www.nxtbook.com/nxtbooks/ieee/smc_202301
https://www.nxtbook.com/nxtbooks/ieee/smc_202210
https://www.nxtbook.com/nxtbooks/ieee/smc_202207
https://www.nxtbook.com/nxtbooks/ieee/smc_202204
https://www.nxtbook.com/nxtbooks/ieee/smc_202201
https://www.nxtbook.com/nxtbooks/ieee/smc_202110
https://www.nxtbook.com/nxtbooks/ieee/smc_202107
https://www.nxtbook.com/nxtbooks/ieee/smc_202104
https://www.nxtbook.com/nxtbooks/ieee/smc_202101
https://www.nxtbook.com/nxtbooks/ieee/smc_202010
https://www.nxtbook.com/nxtbooks/ieee/smc_202007
https://www.nxtbook.com/nxtbooks/ieee/smc_202004
https://www.nxtbook.com/nxtbooks/ieee/smc_202001
https://www.nxtbook.com/nxtbooks/ieee/smc_201910
https://www.nxtbook.com/nxtbooks/ieee/smc_201907
https://www.nxtbook.com/nxtbooks/ieee/smc_201904
https://www.nxtbook.com/nxtbooks/ieee/smc_201901
https://www.nxtbook.com/nxtbooks/ieee/smc_201810
https://www.nxtbook.com/nxtbooks/ieee/smc_201807
https://www.nxtbook.com/nxtbooks/ieee/smc_201804
https://www.nxtbook.com/nxtbooks/ieee/smc_201801
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1017
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0717
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0417
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0117
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1016
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0716
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0416
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0116
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1015
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0715
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0415
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0115
https://www.nxtbookmedia.com