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The high temporal resolution and the asymmetric spatial activations are essential attributes of electroencephalogram (EEG) underlying emotional processes in the brain.
G. E. Schwartz, R. J. Davidson, and F. Maer, “Right hemisphere lateralization for emotion in the human brain: Interactions with cognition,” Science , vol. 190, no. 4211, pp. 286–288, 1975
1975
Earlier work this paper cites.
P. Shammi and D. T. Stuss, “Humour appreciation: a role of the right frontal lobe,” Brain , vol. 122, no. 4, pp. 657–666, 1999
1999
Earlier work this paper cites.
R. J. Dolan, “Emotion, cognition, and behavior,” Science , vol. 298, no. 5596, pp. 1191–1194, 2002
2002
Earlier work this paper cites.
L. Greenberg, “Emotion–focused therapy,” Clinical Psychology & Psychotherapy , vol. 11, no. 1, pp. 3–16, 2004
2004
Earlier work this paper cites.
E. S. Herbener, S. K. Hill, R. W. Marvin, and J. A. Sweeney, “Effects of antipsychotic treatment on emotion perception deficits in first-episode schizophrenia,” American Journal of Psychiatry , vol. 162, no. 9, pp. 1746–1748, 2005
2005
Earlier work this paper cites.
A. B. Craig, “Forebrain emotional asymmetry: a neuroanatomical basis?” Trends in Cognitive Sciences , vol. 9, no. 12, pp. 566 – 571, 2005
2005
Earlier work this paper cites.
Kai Keng Ang, Zheng Yang Chin, Haihong Zhang, and Cuntai Guan, “Filter bank common spatial pattern (FBCSP) in brain-computer interface,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) , June 2008, pp. 2390–2397
2008
Earlier work this paper cites.
K. R. Mickley Steinmetz and E. A. Kensinger, “The effects of valence and arousal on the neural activity leading to subsequent memory,” Psychophysiology , vol. 46, no. 6, pp. 1190–1199, 2009
2009
Earlier work this paper cites.
T. A. Dennis and B. Solomon, “Frontal EEG and emotion regulation: Electrocortical activity in response to emotional film clips is associated with reduced mood induction and attention interference effects,” Biological psychology , vol. 85, no. 3, pp. 456–464, 2010
2010
Earlier work this paper cites.
M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, “A multimodal database for affect recognition and implicit tagging,” IEEE transactions on affective computing , vol. 3, no. 1, pp. 42–55, 2011
2011
Earlier work this paper cites.
S. Koelstra, C. Muhl, M. Soleymani, J. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Patras, “DEAP: A database for emotion analysis using physiological signals,” IEEE Transactions on Affective Computing , vol. 3, no. 1, pp. 18–31, 2012
2012
Earlier work this paper cites.
G. G. Knyazev, “EEG delta oscillations as a correlate of basic homeostatic and motivational processes,” Neuroscience & Biobehavioral Reviews , vol. 36, no. 1, pp. 677–695, 2012
2012
Earlier work this paper cites.
D. Huang, C. Guan, K. K. Ang, H. Zhang, and Y. Pan, “Asymmetric spatial pattern for EEG-based emotion detection,” in The 2012 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2012, pp. 1–7
2012
Earlier work this paper cites.
D. M. Fresco, D. S. Mennin, R. G. Heimberg, and M. Ritter, “Emotion regulation therapy for generalized anxiety disorder,” Cognitive and Behavioral Practice , vol. 20, no. 3, pp. 282 – 300, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
R. D. Lane, L. Ryan, L. Nadel, and L. Greenberg, “Memory reconsolidation, emotional arousal, and the process of change in psychotherapy: New insights from brain science,” Behavioral and Brain Sciences , vol. 38, p. e1, 2015
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2015, pp. 1–9
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv 1502.03167 , 2015
2015
Earlier work this paper cites.
R. S. Duman, G. K. Aghajanian, G. Sanacora, and J. H. Krystal, “Synaptic plasticity and depression: new insights from stress and rapid-acting antidepressants,” Nature Medicine , vol. 22, no. 3, pp. 238–249, 2016
2016
Cited alongside, same era.
J. Atkinson and D. Campos, “Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers,” Expert Systems with Applications , vol. 47, pp. 35 – 41, 2016
2016
Cited alongside, same era.
Y. R. Tabar and U. Halici, “A novel deep learning approach for classification of EEG motor imagery signals,” Journal of Neural Engineering , vol. 14, no. 1, p. 016003, nov 2016
2016
Cited alongside, same era.
H. Goodwin, J. Yiend, and C. R. Hirsch, “Generalized anxiety disorder, worry and attention to threat: A systematic review,” Clinical Psychology Review , vol. 54, pp. 107 – 122, 2017
2017
Cited alongside, same era.
P. Li, H. Liu, Y. Si, C. Li, F. Li, X. Zhu, X. Huang, Y. Zeng, D. Yao, Y. Zhang, and P. Xu, “EEG based emotion recognition by combining functional connectivity network and local activations,” IEEE Transactions on Biomedical Engineering , vol. 66, no. 10, pp. 2869–2881, Oct 2019
2019
Later among the works it cites.
T. Zhang, W. Zheng, Z. Cui, Y. Zong, and Y. Li, “Spatial–temporal recurrent neural network for emotion recognition,” IEEE Transactions on Cybernetics , vol. 49, no. 3, pp. 839–847, March 2019
2019
Later among the works it cites.
F. Fahimi, Z. Zhang, W. B. Goh, T.-S. Lee, K. K. Ang, and C. Guan, “Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI,” Journal of Neural Engineering , vol. 16, no. 2, p. 026007, Jan 2019
2019
Later among the works it cites.
Z. Gao, X. Wang, Y. Yang, C. Mu, Q. Cai, W. Dang, and S. Zuo, “EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation,” IEEE Transactions on Neural Networks and Learning Systems , vol. 30, no. 9, pp. 2755–2763, 2019
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R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, and T. Ball, “Deep learning with convolutional neural networks for EEG decoding and visualization,” Human Brain Mapping , vol. 38, no. 11, pp. 5391–5420, 2017
2017
Cited alongside, same era.
J. K. Carpenter, L. A. Andrews, S. M. Witcraft, M. B. Powers, J. A. J. Smits, and S. G. Hofmann, “Cognitive behavioral therapy for anxiety and related disorders: A meta-analysis of randomized placebo-controlled trials,” Depression and Anxiety , vol. 35, no. 6, pp. 502–514, 2018
2018
Cited alongside, same era.
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,” Journal of Neural Engineering , vol. 15, no. 5, p. 056013, Jul 2018
2018
Cited alongside, same era.
S. Sakhavi, C. Guan, and S. Yan, “Learning temporal information for brain-computer interface using convolutional neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 29, no. 11, pp. 5619–5629, Nov 2018
2018
Cited alongside, same era.
Y. Yang, Q. M. J. Wu, W. Zheng, and B. Lu, “EEG-based emotion recognition using hierarchical network with subnetwork nodes,” IEEE Transactions on Cognitive and Developmental Systems , vol. 10, no. 2, pp. 408–419, June 2018
2018
Cited alongside, same era.
J. Li, Z. Zhang, and H. He, “Hierarchical convolutional neural networks for EEG-based emotion recognition,” Cognitive Computation , vol. 10, no. 2, pp. 368–380, Apr 2018
2018
Cited alongside, same era.
X. Li, D. Song, P. Zhang, Y. Zhang, Y. Hou, and B. Hu, “Exploring EEG features in cross-subject emotion recognition,” Frontiers in Neuroscience , vol. 12, p. 162, 2018
2018
Cited alongside, same era.
Z. Jiao, X. Gao, Y. Wang, J. Li, and H. Xu, “Deep convolutional neural networks for mental load classification based on EEG data,” Pattern Recognition , vol. 76, pp. 582 – 595, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
N. Robinson, S. Lee, and C. Guan, “EEG representation in deep convolutional neural networks for classification of motor imagery,” in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) , Oct 2019, pp. 1322–1326
2019
Later among the works it cites.
Z. Liang, S. Oba, and S. Ishii, “An unsupervised EEG decoding system for human emotion recognition,” Neural Networks , vol. 116, pp. 257 – 268, 2019
2019
Later among the works it cites.
I. Daly, D. Williams, F. Hwang, A. Kirke, E. R. Miranda, and S. J. Nasuto, “Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music,” Scientific reports , vol. 9, no. 1, pp. 1–22, 2019
2019
Later among the works it cites.
Y. Li, W. Zheng, L. Wang, Y. Zong, and Z. Cui, “From regional to global brain: a novel hierarchical spatial-temporal neural network model for EEG emotion recognition,” IEEE Transactions on Affective Computing , 2019
2019
Later among the works it cites.
O.-Y. Kwon, M.-H. Lee, C. Guan, and S.-W. Lee, “Subject-independent brain–computer interfaces based on deep convolutional neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 10, pp. 3839–3852, 2020
2020
Later among the works it cites.
T. Zhang, Z. Cui, C. Xu, W. Zheng, and J. Yang, “Variational pathway reasoning for EEG emotion recognition,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 03, pp. 2709–2716, Apr. 2020
2020
Later among the works it cites.
R. Mane, N. Robinson, A. P. Vinod, S. W. Lee, and C. Guan, “A multi-view CNN with novel variance layer for motor imagery brain computer interface,” in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC) , 2020, pp. 2950–2953
2020
Later among the works it cites.
M. S. Mahmud, F. Ahmed, M. Yeasin, C. Alain, and G. M. Bidelman, “Multivariate models for decoding hearing impairment using EEG gamma-band power spectral density,” in 2020 International Joint Conference on Neural Networks (IJCNN) , 2020, pp. 1–7
2020
Later among the works it cites.
Y. Ding, N. Robinson, Q. Zeng, D. Chen, A. A. Phyo Wai, T. S. Lee, and C. Guan, “TSception:a deep learning framework for emotion detection using EEG,” in 2020 International Joint Conference on Neural Networks (IJCNN) , 2020, pp. 1–7
2020
Later among the works it cites.
A. Appriou, A. Cichocki, and F. Lotte, “Modern machine-learning algorithms: For classifying cognitive and affective states from electroencephalography signals,” IEEE Systems, Man, and Cybernetics Magazine , vol. 6, no. 3, pp. 29–38, 2020
2020
Later among the works it cites.
S. M. Sheppard, L. M. Keator, B. L. Breining, A. E. Wright, S. Saxena, D. C. Tippett, and A. E. Hillis, “Right hemisphere ventral stream for emotional prosody identification: Evidence from acute stroke,” Neurology , vol. 94, no. 10, pp. e1013–e1020, 2020
2020
Later among the works it cites.
Y. Li, W. Zheng, Y. Zong, Z. Cui, T. Zhang, and X. Zhou, “A bi-hemisphere domain adversarial neural network model for EEG emotion recognition,” IEEE Transactions on Affective Computing , vol. 12, no. 2, pp. 494–504, 2021
2021
Closest in time.
Y. Gao, Z. Cao, J. Liu, and J. Zhang, “A novel dynamic brain network in arousal for brain states and emotion analysis,” Mathematical Biosciences and Engineering , vol. 18, no. 6, pp. 7440–7463, 2021
2021
Closest in time.
X. Zhang, J. Liu, J. Shen, S. Li, K. Hou, B. Hu, J. Gao, T. Zhang, and B. Hu, “Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine,” IEEE Transactions on Cybernetics , vol. 51, no. 9, pp. 4386–4399, 2021
2021
Closest in time.