IEEE Computational Intelligence Magazine - May 2021 - 45

we will focus on developing a practical and more plausible
multi-class BCI system with the application of adversarial
learning [47], [48] or other learning strategies.
Acknowledgment

This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the
Korea government under Grant 2017-0-00451 (Development
of BCI based Brain and Cognitive Computing Technology for
Recognizing User's Intentions using Deep Learning) and
Grant 2019-0-00079 (Department of Artificial Intelligence,
Korea University)
This article has supplementary downloadable material available at https://doi.org/10.1109/MCI.2021.3061875, provided
by the authors.
References

[1] B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K.-R. Muller, " Optimizing
Spatial Filters for Robust EEG Single-trial Analysis, " IEEE Signal Process. Mag., vol. 25,
no. 1, pp. 41-56, 2008. doi: 10.1109/MSP.2008.4408441.
[2] Y. Wang, M. Nakanishi, and D. Zhang, " EEG-based brain-computer interfaces, " Adv. Exp. Med. Biol., vol. 1101, pp. 41-65, 2019. doi: 10.1515/revneuro.2010.
21.6.451.
[3] R. T. Schirrmeister et al., " Deep learning with convolutional neural networks for EEG
decoding and visualization, " Hum. Brain Mapp., vol. 38, no. 11, pp. 5391-5420, 2017. doi:
10.1002/hbm.23730.
[4] V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J.
Lance, " EEGNet: A compact convolutional neural network for eeg-based brain-computer interfaces, " J. Neural Eng., vol. 15, no. 5, p. 056013, 2018. doi: 10.1088/1741-2552/
aace8c.
[5] W. Ko, E. Jeon, and H.-I. Suk, " A novel RL-assisted deep learning framework for
task-informative signals selection and classification for spontaneous BCIs, " IEEE Trans.
Ind. Informat., 2020.
[6] P. Aricò, G. Borghini, G. Di Flumeri, N. Sciaraffa, and F. Babiloni, " Passive BCI
beyond the lab: Current trends and future directions, " Physiol. Meas., vol. 39, no. 8, p.
08TR02, 2018. doi: 10.1088/1361-6579/aad57e.
[7] X. Zhang, L. Yao, X. Wang, J. Monaghan, and D. Mcalpine, " A survey on deep
learning based brain computer interface: Recent advances and new frontiers, " 2019,
arXiv:1905.04149.
[8] M.-H. Lee et al., " EEG Dataset and OpenBMI toolbox for three BCI paradigms: An
investigation into BCI illiteracy, " GigaScience, vol. 8, no. 5, p. giz002, 2019. doi: 10.1093/
gigascience/giz002.
[9] M. Nakanishi, Y. Wang, Y.-T. Wang, and T.-P. Jung, " A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials, "
PLoS ONE, vol. 10, no. 10, p. e0140703, 2015. doi: 10.1371/journal.pone.0140703.
[10] J. Jin et al., " The study of generic model set for reducing calibration time in P300based brain-computer interface, " IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 1, pp.
3-12, 2019. doi: 10.1109/TNSRE.2019.2956488.
[11] K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, " Filter bank common spatial pattern
(FBCSP) in brain-computer interface, " in Proc. Int. Joint Conf. Neural Netw. (IJCNN),
2008, pp. 2390-2397.
[12] A. H. Shoeb, " Application of machine learning to epileptic seizure onset detection
and treatment, " Ph.D. dissertation, Massachusetts Inst. Technol., 2009.
[13] H.-I. Suk and S.-W. Lee, " A novel Bayesian framework for discriminative feature
extraction in brain-computer interfaces, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35,
no. 2, pp. 286-299, 2012. doi: 10.1109/TPAMI.2012.69.
[14] A. H. Shoeb and J. V. Guttag, " Application of machine learning to epileptic seizure
detection, " in Proc. 27th Int. Conf. Mach. Learn. (ICML), 2010, pp. 975-982.
[15] A. M. Ray et al., " A subject-independent pattern-based brain-computer interface, "
Front. Behav. Neurosci., vol. 9, p. 269, 2015. doi: 10.3389/fnbeh.2015.00269.
[16] F. Lotte, C. Guan, and K. K. Ang, " Comparison of designs towards a subjectindependent brain-computer interface based on motor imagery, " in Proc. Annu.
Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), 2009, pp. 4543-4546. doi: 10.1109/
IEMBS.2009.5334126.
[17] D. Zhang et al., " Cascade and parallel convolutional recurrent neural networks on
EEG-based intention recognition for brain computer interface, " in Proc. 32nd AAAI Conf.
Artif. Intell. (AAAI), 2018.
[18] W. Ko, J. Yoon, E. Kang, E. Jun, J.-S. Choi, and H.-I. Suk, " Deep recurrent spatiotemporal neural network for motor imagery based BCI, " in Proc. 6th Int. Winter Conf.
Brain-Comput. Interface (BCI), 2018, pp. 1-3.
[19] F. Chollet, " Xception: Deep learning with depthwise separable convolutions, " in
Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 1251-1258.
[20] A. Supratak, H. Dong, C. Wu, and Y. Guo, " DeepSleepNet: A model for automatic
sleep stage scoring based on raw single-channel EEG, " IEEE Trans. Neural Syst. and Rehabil. Eng., vol. 25, no. 11, pp. 1998-2008, 2017. doi: 10.1109/TNSRE.2017.2721116.

[21] S. Sakhavi, C. Guan, and S. Yan, " Learning temporal information for brain-computer interface using convolutional neural networks, " IEEE Trans. Neural Netw. Learn. Syst.,
2018. doi: 10.1109/TNNLS.2018.2789927.
[22] N. Waytowich et al., " Compact convolutional neural networks for classification
of asynchronous steady-state visual evoked potentials, " J. Neural Eng., vol. 15, no. 6, p.
066031, 2018. doi: 10.1088/1741-2552/aae5d8.
[23] N.-S. Kwak, K.-R. Müller, and S.-W. Lee, " A convolutional neural network for
steady state visual evoked potential classification under ambulatory environment, " PLoS
ONE, vol. 12, no. 2, p. e0172578, 2017. doi: 10.1371/journal.pone.0172578.
[24] U. Asif, S. Roy, J. Tang, and S. Harrer, " SeizureNet: A deep convolutional neural network for accurate seizure type classification and seizure detection, " 2019, arXiv:1903.03232.
[25] A. Emami, N. Kunii, T. Matsuo, T. Shinozaki, K. Kawai, and H. Takahashi,
" Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images, " NeuroImage Clin, vol. 22, p. 101,684, 2019. doi: 10.1016/j.
nicl.2019.101684.
[26] Z. Gao et al., " EEG-based spatio-temporal convolutional neural network for
driver fatigue evaluation, " IEEE Trans. Neural Netw. Learn. Syst., 2019. doi: 10.1109/
TNNLS.2018.2886414.
[27] O.-Y. Kwon, M.-H. Lee, C. Guan, and S.-W. Lee, " Subject-independent braincomputer interfaces based on deep convolutional neural networks, " IEEE Trans. Neural
Netw. Learn. Syst, 2019. doi: 10.1109/TNNLS.2019.2946869.
[28] W. Ko, K. Oh, E. Jeon, and H.-I. Suk, " VIGNet: A deep convolutional neural network for EEG-based driver vigilance estimation, " in Proc. 8th Int. Winter Conf. BrainComput. Interface (BCI), 2020, pp. 1-3. doi: 10.1109/BCI48061.2020.9061668.
[29] V. Jayaram, M. Alamgir, Y. Altun, B. Scholkopf, and M. Grosse-Wentrup, " Transfer
learning in brain-computer interfaces, " IEEE Comput. Intell. Mag., vol. 11, no. 1, pp.
20-31, 2016. doi: 10.1109/MCI.2015.2501545.
[30] S. Haufe et al., " On the interpretation of weight vectors of linear models in multivariate neuroimaging, " NeuroImage, vol. 87, pp. 96-110, 2014. doi: 10.1016/j.neuroimage.2013.10.067.
[31] J. Jin, Y. Miao, I. Daly, C. Zuo, D. Hu, and A. Cichocki, " Correlation-based channel
selection and regularized feature optimization for MI-based BCI, " Neural Netw., vol. 118,
pp. 262-270, 2019. doi: 10.1016/j.neunet.2019.07.008.
[32] J. Jin et al., " Bispectrum-based channel selection for motor imagery based braincomputer interfacing, " IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 10, pp. 2153-
2163, 2020. doi: 10.1109/TNSRE.2020.3020975.
[33] J. Jin, R. Xiao, I. Daly, Y. Miao, X. Wang, and A. Cichocki, " Internal feature selection method of CSP based on L1-norm and Dempster-Shafer theory, " IEEE Trans. Neural
Netw. Learn. Syst., 2020. doi: 10.1109/TNNLS.2020.3015505.
[34] F. Lotte, " Signal processing approaches to minimize or suppress calibration time in
oscillatory activity-based brain-computer interfaces, " Proc. IEEE, vol. 103, no. 6, pp.
871-890, 2015. doi: 10.1109/JPROC.2015.2404941.
[35] T. H. Sanders, M. McCurry, and M. A. Clements, " Sleep stage classification with
cross frequency coupling, " in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC),
2014, pp. 4579-4582. doi: 10.1109/EMBC.2014.6944643.
[36] W.-L. Zheng and B.-L. Lu, " A multimodal approach to estimating vigilance using EEG and Forehead EOG, " J. Neural Eng., vol. 14, no. 2, p. 026017, 2017. doi:
10.1088/1741-2552/aa5a98.
[37] J. Lee et al., " Early seizure detection by applying frequency-based algorithm derived
from the principal component analysis, " Front. Neuroinform., vol. 11, p. 52, 2017. doi:
10.3389/fninf.2017.00052.
[38] M. Liang and X. Hu, " Recurrent convolutional neural network for object recognition, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), 2015, pp.
3367-3375. doi: 10.1109/CVPR.2015.7298958.
[39] M. Lin, Q. Chen, and S. Yan, " Network in NETWORK, " 2013, arXiv:1312.4400.
[40] H. Cho, M. Ahn, S. Ahn, M. Kwon, and S. C. Jun, " EEG datasets for motor imagery brain-computer interface, " GigaScience, vol. 6, no. 7, p. gix034, 2017. doi: 10.1093/
gigascience/gix034.
[41] X. Glorot and Y. Bengio, " Understanding the difficulty of training deep feedforward neural networks, " in Proc. 13th Int. Conf. Artif. Intell. Statist. (AISTATS), 2010, pp.
249-256.
[42] G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller, " Explaining
nonlinear classification decisions with deep Taylor decomposition, " Pattern Recognit., vol.
65, pp. 211-222, 2017. doi: 10.1016/j.patcog.2016.11.008.
[43] F. Hutter, L. Kotthoff, and J. Vanschoren, Automated Machine Learning: Methods, Systems, Challenges. Springer-Verlag, 2019.
[44] M. Kim, M.-K. Kim, M. Hwang, H.-Y. Kim, J. Cho, and S.-P. Kim, " Online home
appliance control using EEG-based brain-computer interfaces, " Electronics, vol. 8, no. 10,
p. 1101, 2019. doi: 10.3390/electronics8101101.
[45] S.-H. Lee, M. Lee, and S.-W. Lee, " Neural decoding of imagined speech and visual
imagery as intuitive paradigms for BCI communication, " IEEE Trans. Neural Syst. Rehabil. Eng, 2020. doi: 10.1109/TNSRE.2020.3040289.
[46] E. Rapaport, O. Shriki, and R. Puzis, " EEGNAS: Neural architecture search for
electroencephalography data analysis and decoding, " in Proc. Int. Work. Hum. Brain Artif.
Intell., 2019, pp. 3-20. doi: 10.1007/978-981-15-1398-5_1.
[47] Y. Ganin et al., " Domain-adversarial training of neural networks, " J. Mach. Learn.
Res., vol. 17, no. 1, pp. 2096-2030, 2016. doi: 10.5555/2946645.2946704.
[48] E. Jeon, W. Ko, and H.-I. Suk, " Domain adaptation with source selection for motorimagery based BCI, " in Proc. 7th Int. Winter Conf. Brain-Comput. Interface (BCI), 2019, pp.
1-4. doi: 10.1109/IWW-BCI.2019.8737340.



MAY 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

45


https://www.doi.org/10.1109/MCI.2021.3061875

IEEE Computational Intelligence Magazine - May 2021

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - May 2021

Contents
IEEE Computational Intelligence Magazine - May 2021 - Cover1
IEEE Computational Intelligence Magazine - May 2021 - Cover2
IEEE Computational Intelligence Magazine - May 2021 - Contents
IEEE Computational Intelligence Magazine - May 2021 - 2
IEEE Computational Intelligence Magazine - May 2021 - 3
IEEE Computational Intelligence Magazine - May 2021 - 4
IEEE Computational Intelligence Magazine - May 2021 - 5
IEEE Computational Intelligence Magazine - May 2021 - 6
IEEE Computational Intelligence Magazine - May 2021 - 7
IEEE Computational Intelligence Magazine - May 2021 - 8
IEEE Computational Intelligence Magazine - May 2021 - 9
IEEE Computational Intelligence Magazine - May 2021 - 10
IEEE Computational Intelligence Magazine - May 2021 - 11
IEEE Computational Intelligence Magazine - May 2021 - 12
IEEE Computational Intelligence Magazine - May 2021 - 13
IEEE Computational Intelligence Magazine - May 2021 - 14
IEEE Computational Intelligence Magazine - May 2021 - 15
IEEE Computational Intelligence Magazine - May 2021 - 16
IEEE Computational Intelligence Magazine - May 2021 - 17
IEEE Computational Intelligence Magazine - May 2021 - 18
IEEE Computational Intelligence Magazine - May 2021 - 19
IEEE Computational Intelligence Magazine - May 2021 - 20
IEEE Computational Intelligence Magazine - May 2021 - 21
IEEE Computational Intelligence Magazine - May 2021 - 22
IEEE Computational Intelligence Magazine - May 2021 - 23
IEEE Computational Intelligence Magazine - May 2021 - 24
IEEE Computational Intelligence Magazine - May 2021 - 25
IEEE Computational Intelligence Magazine - May 2021 - 26
IEEE Computational Intelligence Magazine - May 2021 - 27
IEEE Computational Intelligence Magazine - May 2021 - 28
IEEE Computational Intelligence Magazine - May 2021 - 29
IEEE Computational Intelligence Magazine - May 2021 - 30
IEEE Computational Intelligence Magazine - May 2021 - 31
IEEE Computational Intelligence Magazine - May 2021 - 32
IEEE Computational Intelligence Magazine - May 2021 - 33
IEEE Computational Intelligence Magazine - May 2021 - 34
IEEE Computational Intelligence Magazine - May 2021 - 35
IEEE Computational Intelligence Magazine - May 2021 - 36
IEEE Computational Intelligence Magazine - May 2021 - 37
IEEE Computational Intelligence Magazine - May 2021 - 38
IEEE Computational Intelligence Magazine - May 2021 - 39
IEEE Computational Intelligence Magazine - May 2021 - 40
IEEE Computational Intelligence Magazine - May 2021 - 41
IEEE Computational Intelligence Magazine - May 2021 - 42
IEEE Computational Intelligence Magazine - May 2021 - 43
IEEE Computational Intelligence Magazine - May 2021 - 44
IEEE Computational Intelligence Magazine - May 2021 - 45
IEEE Computational Intelligence Magazine - May 2021 - 46
IEEE Computational Intelligence Magazine - May 2021 - 47
IEEE Computational Intelligence Magazine - May 2021 - 48
IEEE Computational Intelligence Magazine - May 2021 - 49
IEEE Computational Intelligence Magazine - May 2021 - 50
IEEE Computational Intelligence Magazine - May 2021 - 51
IEEE Computational Intelligence Magazine - May 2021 - 52
IEEE Computational Intelligence Magazine - May 2021 - 53
IEEE Computational Intelligence Magazine - May 2021 - 54
IEEE Computational Intelligence Magazine - May 2021 - 55
IEEE Computational Intelligence Magazine - May 2021 - 56
IEEE Computational Intelligence Magazine - May 2021 - 57
IEEE Computational Intelligence Magazine - May 2021 - 58
IEEE Computational Intelligence Magazine - May 2021 - 59
IEEE Computational Intelligence Magazine - May 2021 - 60
IEEE Computational Intelligence Magazine - May 2021 - 61
IEEE Computational Intelligence Magazine - May 2021 - 62
IEEE Computational Intelligence Magazine - May 2021 - 63
IEEE Computational Intelligence Magazine - May 2021 - 64
IEEE Computational Intelligence Magazine - May 2021 - 65
IEEE Computational Intelligence Magazine - May 2021 - 66
IEEE Computational Intelligence Magazine - May 2021 - 67
IEEE Computational Intelligence Magazine - May 2021 - 68
IEEE Computational Intelligence Magazine - May 2021 - 69
IEEE Computational Intelligence Magazine - May 2021 - 70
IEEE Computational Intelligence Magazine - May 2021 - 71
IEEE Computational Intelligence Magazine - May 2021 - 72
IEEE Computational Intelligence Magazine - May 2021 - 73
IEEE Computational Intelligence Magazine - May 2021 - 74
IEEE Computational Intelligence Magazine - May 2021 - 75
IEEE Computational Intelligence Magazine - May 2021 - 76
IEEE Computational Intelligence Magazine - May 2021 - 77
IEEE Computational Intelligence Magazine - May 2021 - 78
IEEE Computational Intelligence Magazine - May 2021 - 79
IEEE Computational Intelligence Magazine - May 2021 - 80
IEEE Computational Intelligence Magazine - May 2021 - 81
IEEE Computational Intelligence Magazine - May 2021 - 82
IEEE Computational Intelligence Magazine - May 2021 - 83
IEEE Computational Intelligence Magazine - May 2021 - 84
IEEE Computational Intelligence Magazine - May 2021 - 85
IEEE Computational Intelligence Magazine - May 2021 - 86
IEEE Computational Intelligence Magazine - May 2021 - 87
IEEE Computational Intelligence Magazine - May 2021 - 88
IEEE Computational Intelligence Magazine - May 2021 - 89
IEEE Computational Intelligence Magazine - May 2021 - 90
IEEE Computational Intelligence Magazine - May 2021 - 91
IEEE Computational Intelligence Magazine - May 2021 - 92
IEEE Computational Intelligence Magazine - May 2021 - 93
IEEE Computational Intelligence Magazine - May 2021 - 94
IEEE Computational Intelligence Magazine - May 2021 - 95
IEEE Computational Intelligence Magazine - May 2021 - 96
IEEE Computational Intelligence Magazine - May 2021 - 97
IEEE Computational Intelligence Magazine - May 2021 - 98
IEEE Computational Intelligence Magazine - May 2021 - 99
IEEE Computational Intelligence Magazine - May 2021 - 100
IEEE Computational Intelligence Magazine - May 2021 - Cover3
IEEE Computational Intelligence Magazine - May 2021 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202311
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202308
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202305
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202302
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202211
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202208
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202205
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202202
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202111
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202108
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202105
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202102
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202011
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202008
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202005
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202002
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201911
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201908
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201905
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201902
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201811
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201808
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201805
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201802
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter12
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall12
https://www.nxtbookmedia.com