750
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

A novel brain inception neural network model using EEG graphic structure for emotion recognition

, , , , , , , , & ORCID Icon show all
Article: 2222159 | Received 07 Nov 2022, Accepted 01 Jun 2023, Published online: 16 Jun 2023

References

  • Poria S, Cambria E, Bajpai R, et al. A review of affective computing: from unimodal analysis to multimodal fusion. Inf Fusion. 2017;37:98–125. doi: 10.1016/j.inffus.2017.02.003.
  • Joshi VM, Ghongade RB. IDEA: intellect database for emotion analysis using EEG signal. J King Saud Univ-Comp Inf Sci. 2022;34(7):4433–4447. doi: 10.1016/j.jksuci.2020.10.007.
  • Gao X, Huang W, Liu Y, et al. A novel robust student’s t-based granger causality for EEG based brain network analysis. Biomed Signal Process Control. 2023;80:104321. doi: 10.1016/j.bspc.2022.104321.
  • Bocharov AV, Knyazev GG, Savostyanov AN. Depression and implicit emotion processing: an EEG study. Neurophysiol Clin. 2017;47(3):225–230. doi: 10.1016/j.neucli.2017.01.009.
  • Yu M, Xiao S, Hua M, et al. EEG-based emotion recognition in an immersive virtual reality environment: from local activity to brain network features. Biomed Signal Process Control. 2022;72:103349. doi: 10.1016/j.bspc.2021.103349.
  • Lee GP, Meador KJ, Loring DW, et al. Neural substrates of emotion as revealed by functional magnetic resonance imaging. Cogn Behav Neurol. 2004;17(1):9–17. doi: 10.1097/00146965-200403000-00002.
  • Soleymani M, Pantic M, Pun T. Multimodal emotion recognition in response to videos. IEEE Trans. Affective Comput. 2012;3(2):211–223. doi: 10.1109/T-AFFC.2011.37.
  • Hoeser T, Kuenzer C. Object detection and image segmentation with deep learning on earth observation data: a review-part i: evolution and recent trends. Remote Sens. 2020;12(10):1667. doi: 10.3390/rs12101667.
  • Eykholt K, et al. Robust physical-world attacks on deep learning visual classification. Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. p. 1625–1634. doi: 10.1109/CVPR.2018.00175
  • Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. doi: 10.1016/j.media.2017.07.005.
  • Topic A, Russo M. Emotion recognition based on EEG feature maps through deep learning network. Eng SciTechnolInt J. 2021;24(6):1442–1454. doi: 10.1016/j.jestch.2021.03.012.
  • Salama ES, El-Khoribi RA, Shoman ME, et al. EEG-based emotion recognition using 3D convolutional neural networks. IJACSA. 2018;9(8):329–337. doi: 10.14569/IJACSA.2018.090843.
  • Zheng W-L, Lu B-L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, Vol. 2015;7(3):162–175.
  • Zheng W-L, Guo H-T, Lu B-L. Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network. in 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015. pp. 154–157: IEEE. doi: 10.1109/NER.2015.7146583.
  • Wang Y, Wu Q, Wang C, et al. DE-CNN: an improved identity recognition algorithm based on the emotional electroencephalography. Comput Math Methods Med. 2020;2020:1–12. doi: 10.1155/2020/7574531.
  • Hwang S, Hong K, Son G, et al. Learning CNN features from DE features for EEG-based emotion recognition. Pattern Anal Applic. 2020;23(3):1323–1335. doi: 10.1007/s10044-019-00860-w.
  • Wang X-h, Zhang T, Xu X-m, et al. EEG emotion recognition using dynamical graph convolutional neural networks and broad learning system. in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid, SPAIN, DEC, 03-06, 2018. pp. 1240–1244: IEEE. doi: 10.1109/BIBM.2018.8621147.
  • Li P, Huang X, Zhu X, et al. Lp (p≤ 1) norm partial directed coherence for directed network analysis of scalp EEGs. Brain Topogr. 2018;31(5):738–752. doi: 10.1007/s10548-018-0624-0.
  • Wang Z-M, Zhou R, He Y, et al. Functional integration and separation of brain network based on phase locking value during emotion processing. IEEE Transactions on Cognitive and Developmental Systems, 2020;99:1. doi: 10.1109/TCDS.2020.3001642.
  • Šverko Z, Vrankić M, Vlahinić S, et al. Complex Pearson correlation coefficient for EEG connectivity analysis. Sensors, Vol. 2022;22(4):1477. doi: 10.3390/s22041477.
  • Wang Z, Tong Y, Heng X. Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access. 2019;7:93711–93722. doi: 10.1109/ACCESS.2019.2927768.
  • Gonuguntla V, Mallipeddi R, Veluvolu KC. Identification of emotion associated brain functional network with phase locking value. in 2016 38Th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc). IEEE 2016. pp. 4515–4518: doi: 10.1109/EMBC.2016.7591731.
  • Dongwei C, Fang W, Zhen W, et al. EEG-based emotion recognition with brain network using independent components analysis and granger causality. in 2013 International Conference on computer medical applications (ICCMA) IEEE. Sousse, TUNISIA, JAN. 20-22, 2013. pp. 1–6. doi: 10.1109/ICCMA.2013.6506157.
  • Chen D, Miao R, Deng Z, et al. Sparse granger causality analysis model based on sensors correlation for emotion recognition classification in electroencephalography. Front Comput Neurosci. 2021;15:684373. doi: 10.3389/fncom.2021.684373.
  • Tang C, Li Y, Chen B. Comparison of cross-subject EEG emotion recognition algorithms in the BCI controlled robot contest in world robot contest 2021. Brain Sci Adv. 2022;8(2):142–152. doi: 10.26599/BSA.2022.9050013.
  • Pan SJ, Tsang IW, Kwok JT, et al. Domain adaptation via transfer component analysis. IEEE Trans Neural Netw. 2011;22(2):199–210. doi: 10.1109/TNN.2010.2091281.
  • Li Z, Jing X-Y, Wu F, et al. Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction. Autom Softw Eng. 2018;25(2):201–245. doi: 10.1007/s10515-017-0220-7.
  • Ilse M, Tomczak JM, Louizos C, et al. "Diva: domain invariant variational autoencoders," in Medical Imaging with Deep Learning, 2020. pp. 322–348: PMLR.
  • Luo Y, Lu B-L. Wasserstein-distance-based multi-source adversarial domain adaptation for emotion recognition and vigilance estimation. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Online conference, Dec. 9-12, 2021. pp. 1424–1428: IEEE. doi: 10.1109/BIBM52615.2021.9669383.
  • Lin Y-P, Jung T-P. Improving EEG-based emotion classification using conditional transfer learning. Front Hum Neurosci. 2017;11:334. doi: 10.3389/fnhum.2017.00334.
  • Li J, Qiu S, Shen Y-Y, et al. Multisource transfer learning for cross-subject EEG emotion recognition. IEEE Trans Cybern. 2020;50(7):3281–3293. doi: 10.1109/TCYB.2019.2904052.
  • Szegedy C, et al. Going deeper with convolutions in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. p. 1–9.
  • Li P, Liu H, Si Y, et al. EEG based emotion recognition by combining functional connectivity network and local activations. IEEE Trans Biomed Eng. 2019;66(10):2869–2881. doi: 10.1109/TBME.2019.2897651.
  • Zhang J, Wang N, Kuang H, et al. An improved method to calculate phase locking value based on Hilbert–Huang transform and its application. Neural Comput & Applic. 2014;24(1):125–132. doi: 10.1007/s00521-013-1510-z.
  • Li P, Huang X, Zhu X, et al. Robust brain causality network construction based on bayesian multivariate autoregression. Biomed Signal Process Control. 2020;58:101864. doi: 10.1016/j.bspc.2020.101864.
  • Seth AK. A MATLAB toolbox for granger causal connectivity analysis. J Neurosci Methods. 2010;186(2):262–273. doi: 10.1016/j.jneumeth.2009.11.020.
  • Bressler SL, Seth AK. Wiener–granger causality: a well established methodology. Neuroimage. 2011;58(2):323–329. doi: 10.1016/j.neuroimage.2010.02.059.
  • Yu R, Zhang H, An L, et al. Connectivity strength‐weighted sparse group representation‐based brain network construction for M CI classification. Hum Brain Mapp. 2017;38(5):2370–2383. doi: 10.1002/hbm.23524.
  • Shaw L, Routray A. A new framework to infer intra-and inter-brain sparse connectivity estimation for eeg source information flow. IEEE Sensors J. 2018;18(24):10134–10144. doi: 10.1109/JSEN.2018.2875377.
  • Efron B, Hastie T, Johnstone I "Least angle regression,", et al. 2004.
  • Ganin Y, Lempitsky V. "Unsupervised domain adaptation by backpropagation," in International conference on machine learning, 2015. pp. 1180–1189: PMLR.
  • Murtagh F. Multilayer perceptrons for classification and regression. Neurocomputing. 1991;2(5-6):183–197. doi: 10.1016/0925-2312(91)90023-5.
  • Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565–1567. doi: 10.1038/nbt1206-1565.
  • Zheng W-L, Zhu J-Y, Lu B-L. Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affective Comput. 2019;10(3):417–429. doi: 10.1109/TAFFC.2017.2712143.
  • Wilke C, Ding L, He B. Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function. IEEE Trans Biomed Eng. 2008;55(11):2557–2564. doi: 10.1109/TBME.2008.919885.
  • Wei X, Zhou L, Chen Z, et al. Automatic seizure detection using three-dimensional CNN based on multi-channel EEG. BMC Med Inform Decis Mak. 2018;18(S5):71–80. doi: 10.1186/s12911-018-0693-8.
  • van Diessen E, Numan T, van Dellen E, et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin Neurophysiol. 2015;126(8):1468–1481. doi: 10.1016/j.clinph.2014.11.018.