Ahmed F, Bari ASMH, Gavrilova ML (2019) Emotion recognition from body movement. IEEE Access 8:11761–11781

Article  Google Scholar 

Borgwardt KM, Gretton A, Rasch MJ et al (2006) Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics 22(14):e49–e57

Article  CAS  PubMed  Google Scholar 

Bozhokin SV, Suslova IB (2015) Wavelet-based analysis of spectral rearrangements of EEG patterns and of non-stationary correlations[J]. Physica A 421:151–160

Article  MathSciNet  Google Scholar 

Chai X, Wang Q, Zhao Y et al (2016) Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition[J]. Comput Biol Med 79:205–214

Article  PubMed  Google Scholar 

Chen H, Jin M, Li Z et al (2021) MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition [J]. Front Neurosci 15:778488

Article  PubMed  PubMed Central  Google Scholar 

Chen H, Li Z, Jin M, et al (2021) MEERNet: multi-source EEG-based emotion recognition network for generalization across subjects and sessions[C]//2021 In: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, pp 6094–6097

Cowie R, Douglas-Cowie E, Tsapatsoulis N et al (2001) Emotion recognition in human-computer interaction [J]. IEEE Signal Process Mag 18(1):32–80

Article  ADS  Google Scholar 

Dolan RJ (2002) Emotion, cognition, and behavior [J]. Science 298(5596):1191–1194

Article  ADS  CAS  PubMed  Google Scholar 

Duan RN, Zhu JY, Lu BL (2013) Differential entropy feature for EEG-based emotion classification[C]//2013 In: 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, pp 81–84

Feng X, Cong P, Dong L et al (2023) Channel attention convolutional aggregation network based on video-level features for EEG emotion recognition [J]. Cognit Neurodynamics. https://doi.org/10.1007/s11571-023-10034-4

Article  Google Scholar 

Ganin Y, Ustinova E, Ajakan H et al (2016) Domain-adversarial training of neural networks [J]. J Mach Learn Res 17(1):1–35

MathSciNet  Google Scholar 

Ghare PS, Paithane AN (2016) Human emotion recognition using non-linear and non-stationary EEG signal[C]//2016 In: International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, pp 1013–1016

Gu X, Cai W, Gao M et al (2022) Multi-source domain transfer discriminative dictionary learning modeling for electroencephalogram-based emotion recognition [J]. IEEE Trans Comput Soc Syst 9(6):1604–1612

Article  Google Scholar 

Hang W, Feng W, Du R et al (2019) Cross-subject EEG signal recognition using deep domain adaptation network[J]. IEEE Access 7:128273–128282

Article  Google Scholar 

Hinton G, van der Maaten L (2008) Visualizing data using t-SNE. J Mach Learn Res [J] 9:2579

Google Scholar 

Imran A, Athitsos V M (2021) Adaptive feature norm for unsupervised subdomain adaptation [C]//International Symposium on Visual Computing. Springer, Cham, pp 341-352

Jin YM, Luo YD, Zheng WL, et al (2017) EEG-based emotion recognition using domain adaptation network[C]//2017 In: international conference on orange technologies (ICOT). IEEE, pp 222–225

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization [J]. arXiv preprint arXiv:1412.6980

LiH, Jin YM, Zheng WL, et al (2018) Cross-subject emotion recognition using deep adaptation networks[C]//In: International conference on neural information processing. Springer, Cham, pp 403–413.

Liu ZT, Xie Q, Wu M et al (2018) Speech emotion recognition based on an improved brain emotion learning model [J]. Neurocomputing 309:145–156

Article  Google Scholar 

Long M, Wang J (2015) Learning transferable features with deep adaptation networks[C]// JMLR.org. JMLR.org

Lotte F, Guan C (2010) Learning from other subjects helps reducing brain-computer interface calibration time[C]//2010 In: IEEE International conference on acoustics, speech and signal processing. IEEE, pp 614–617

Mithbavkar SA, Shah MS (2021) Analysis of EMG based emotion recognition for multiple people and emotions[C]//2021 In: IEEE 3rd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE, pp 1–4

Shin Y, Lee S, Ahn M et al (2015) Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification[J]. Biomed Signal Process Control 21:8–18

Article  Google Scholar 

Tzeng E, Hoffman J, Zhang N, et al (2014) Deep domain confusion: maximizing for domain invariance [J]. arXiv preprint arXiv:1412.3474

Tzeng E, Hoffman J, Saenko K, et al (2017) Adversarial discriminative domain adaptation [C]//In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7167–7176

Wu G, Liu G, Hao M (2010) The analysis of emotion recognition from GSR based on PSO[C]//2010 In: International symposium on intelligence information processing and trusted computing. IEEE, pp 360–363

Xu R, Li G, Yang J, et al (2018) Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation[J]

Yosinski J, Clune J, Bengio Y, et al (2014) How transferable are features in deep neural networks? [J]. MIT Press

Zhao L M, Yan X, Lu B L (2021) Plug-and-play domain adaptation for cross-subject EEG-based emotion recognition[C]//In: Proceedings of the AAAI Conference on Artificial Intelligence., vol 35(1): pp 863–870

Zheng WL, Lu BL (2015) Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks[J]. IEEE Trans Auton Ment Dev 7(3):162–175

Article  Google Scholar 

Zheng WL, Liu W, Lu Y et al (2018) Emotionmeter: a multimodal framework for recognizing human emotions[J]. IEEE Trans Cybern 49(3):1110–1122

Article  PubMed  Google Scholar 

Zheng W L, Zhang Y Q, Zhu J Y, et al (2015) Transfer components between subjects for EEG-based emotion recognition[C]//2015 In: international conference on affective computing and intelligent interaction (ACII). IEEE, pp 917–922

Zhou R, Zhang Z, Fu H, et al (2023) PR-PL: a novel prototypical representation based pairwise learning framework for emotion recognition using eeg signals[J]. IEEE Transactions on Affective Computing