EEG emotion recognition based on an innovative information potential index

Alhagry S, Fahmy AA, El-Khoribi RA (2017) Emotion recognition based on EEG using LSTM recurrent neural network. Int J Adv Comput Sci Appl IJACSA 8(10):355–358

Google Scholar 

Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A (2017) Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review. Appl Sci 7(12):1239. https://doi.org/10.3390/app7121239

Article  Google Scholar 

Aydın S (2020) Deep learning classification of neuro-emotional phase domain complexity levels induced by affective video film clips. IEEE J Biomed Health Inform 24(6):1695–1702. https://doi.org/10.1109/JBHI.2019.2959843

Article  PubMed  Google Scholar 

Bertolazzi E, Frego M (2015) Preconditioning complex symmetric linear systems. Math Prob Eng 2015:20. https://doi.org/10.1155/2015/548609

Article  MathSciNet  Google Scholar 

Bulagang AF, Weng NG, Mountstephens J, Teo J (2020) A review of recent approaches for emotion classification using electrocardiography and electrodermography signals. Inform Med Unlocked 20:100363

Article  Google Scholar 

Chen J, Min C, Wang C, Tang Z, Liu Y, Hu X (2022) Electroencephalograph-based emotion recognition using brain connectivity feature and domain adaptive residual convolution model. Front Neurosci 16:878146. https://doi.org/10.3389/fnins.2022.878146

Article  PubMed  PubMed Central  Google Scholar 

Chen Y, Zhang H, Long J, Xie Y (2023) Temporal shift residual network for EEG-based emotion recognition: a 3D feature image sequence approach. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-17142-7

Article  PubMed  PubMed Central  Google Scholar 

Cizmeci H, Ozcan C (2022) Enhanced deep capsule network for EEG-based emotion recognition. SIViP. https://doi.org/10.1007/s11760-022-02251-x

Article  Google Scholar 

Czarnecki WM, Tabor J (2017) Extreme entropy machines: robust information theoretic classification. Pattern Anal Appl 20:383–400

Article  MathSciNet  Google Scholar 

Demir F, Sobahi N, Siuly S, Sengur A (2021) Exploring deep learning features for automatic classification of human emotion using EEG rhythms. IEEE Sensors J 21(13):14923–14930

Article  ADS  Google Scholar 

Egger M, Ley M, Hanke S (2019) Emotion recognition from physiological signal analysis: a review. Electro Notes Theor Comput Sci 343:35–55

Article  Google Scholar 

Feng H, Golshan HM, Mahoor MH (2018) A wavelet-based approach to emotion classification using EDA signals. Expert Syst Appl 112:77–86

Article  Google Scholar 

Ghosh D, Sengupta R, Sanyal S, Banerjee A (2018) Emotions from Hindustani classical music: an EEG based study including neural hysteresis. In: Musicality of human brain through fractal analytics. Springer, Singapore, pp 49–72. https://doi.org/10.1007/978-981-10-6511-8_3

Goshvarpour A, Goshvarpour A (2018) A novel feature level fusion for HRV classification using correntropy and Cauchy-Schwarz divergence. J Med Syst 42:109. https://doi.org/10.1007/s10916-018-0961-2

Article  PubMed  Google Scholar 

Goshvarpour A, Goshvarpour A (2020) A novel approach for EEG electrode selection in automated emotion recognition based on lagged Poincare’s indices and sLORETA. Cogn Comput 12:602–618. https://doi.org/10.1007/s12559-019-09699-z

Article  Google Scholar 

Goshvarpour A, Goshvarpour A (2022) Innovative Poincare’s plot asymmetry descriptors for EEG emotion recognition. Cogn Neurodyn 16:545–559. https://doi.org/10.1007/s11571-021-09735-5

Article  PubMed  Google Scholar 

Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M et al (2008) Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil 5:25–58. https://doi.org/10.1186/1743-0003-5-210.1186/1743-0003-5-25

Article  PubMed  PubMed Central  Google Scholar 

Hou HR, Zhang XN, Meng QH (2020) Odor-induced emotion recognition based on average frequency band division of EEG signals. J Neurosci Methods 334:108599

Article  PubMed  Google Scholar 

Huang D, Chen S, Liu C, Zheng L, Tian Z, Jiang D (2021) Differences first in asymmetric brain: a bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition. Neurocomputing. https://doi.org/10.1016/j.neucom.2021.03.105

Article  PubMed  PubMed Central  Google Scholar 

Jenke R, Peer A, Buss M (2014) Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affect Comput 5(3):327–339

Article  Google Scholar 

Khalili Z, Moradi MH (2009) Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG. In: Proceedings of the 2009 international joint conference on neural networks. IEEE Press, New York, pp 1571–1575

Khare SK, Bajaj V (2020) Time–frequency representation and convolutional neural network-based emotion recognition. IEEE Trans Neural Networks Learn Syst 32(7):2901–2909

Article  Google Scholar 

Kılıç B, Aydın S (2022) Classification of contrasting discrete emotional states indicated by EEG based graph theoretical network measures. Neuroinformatics 20(4):863–877. https://doi.org/10.1007/s12021-022-09579-2

Article  PubMed  Google Scholar 

Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2012) DEAP: a database for emotion analysis using physiological signals. IEEE Trans Affect Comput 3:18–31

Article  Google Scholar 

Li W, Zhang Z, Song A (2021) Physiological-signal-based emotion recognition: an odyssey from methodology to philosophy. Measurement 172:108747

Article  Google Scholar 

Lin O, Liu G-Y, Yang J-M, Du Y-Z (2015) Neurophysiological markers of identifying regret by 64 channels EEG signal. In: 12th International computer conference on wavelet active media technology and information processing (ICCWAMTIP), 18–20 Dec. 2015, Chengdu, China, pp 395–399. https://doi.org/10.1109/ICCWAMTIP.2015.7494017

Lin X, Chen J, Ma W, Tang W, Wang Y (2023) EEG emotion recognition using improved graph neural network with channel selection. Comput Methods Programs Biomed 231:107380. https://doi.org/10.1016/j.cmpb.2023.107380

Article  PubMed  Google Scholar 

Luo Y, Wu G, Qiu S, Yang S, Li W, Bi Y (2020) EEG-based emotion classification using deep neural network and sparse autoencoder. Front Syst Neurosci 14:43

Article  PubMed  PubMed Central  Google Scholar 

Maffei A, Angrilli A (2019) Spontaneous blink rate as an index of attention and emotion during film clips viewing. Physiol Behav 204:256–263

Article  CAS  PubMed  Google Scholar 

Miao M, Zheng L, Xu B, Yang Z, Hu W (2023) A multiple frequency bands parallel spatial–temporal 3D deep residual learning framework for EEG-based emotion recognition. Biomed Signal Process Control 79(2):104141. https://doi.org/10.1016/j.bspc.2022.104141

Article  Google Scholar 

Naser DS, Saha G (2021) Influence of music liking on EEG based emotion recognition. Biomed Signal Process Control 64:102251

Article  Google Scholar 

Özerdem MS, Polat H (2017) Emotion recognition based on EEG features in movie clips with channel selection. Brain Inform 4(4):241–252

Article  PubMed  PubMed Central  Google Scholar 

Pane ES, Wibawa AD, Purnomo MH (2019) Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters. Cogn Process 20(4):405–417

Article  PubMed  Google Scholar 

Patel PR, Annavarapu RN (2021) EEG-based human emotion recognition using entropy as a feature extraction measure. Brain Inf. 8:20. https://doi.org/10.1186/s40708-021-00141-5

Article  Google Scholar 

Principe JC (2010) Information theoretic learning: Renyi’s entropy and kernel perspectives. Information science and statistics. Springer, New York

Book  Google Scholar 

Salama ES, El-Khoribi RA, Shoman ME, Shalaby MAW (2018) EEG-based emotion recognition using 3D convolutional neural networks. Int J Adv Comput Sci Appl 9(8):329–337

Google Scholar 

Salankar N, Mishra P, Garg L (2021) Emotion recognition from EEG signals using empirical mode decomposition and second-order difference plot. Biomed Signal Process Control 65:102389

Article  Google Scholar 

Sanyal S, Banerjee A, Basu M, Nag S, Ghosh D, Karmakar S (2020) Do musical notes correlate with emotions? A neuro-acoustical study with Indian classical music. Proc Mtgs Acoust 42(1):035005

Article  Google Scholar 

Seth S, Príncipe JC (2009) On speeding up computation in information theoretic learning. In: International joint conference on neural networks (IJCNN). 14–19 June 2009, Atlanta, pp 2883–2887

Sheng W, Li X (2021) Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network. Pattern Recognit 114:107868

Article  Google Scholar 

Siddharth T-PJ, Sejnowski TJ (2022) Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing. IEEE Trans Affect Comput 13(1):96–107. https://doi.org/10.1109/TAFFC.2019.2916015

Article  Google Scholar 

Silva R, Salvador G, Bota P et al (2022) Impact of sampling rate and interpolation on photoplethysmography and electrodermal activity signals’ waveform morphology and feature extraction. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07212-6

Article  PubMed  PubMed Central  Google Scholar 

Tuncer T, Dogan S, Subasi A (2021) A new fractal pattern feature generation function based emotion recognition method using EEG. Chaos Soliton Fract 144:110671

Article  MathSciNet  Google Scholar 

Van de Steen F, Faes L, Karahan E, Songsiri J, Valdes-Sosa PA, Marinazzo D (2019) Critical comments on EEG sensor space dynamical connectivity analysis. Brain Topogr 32:643–654. https://doi.org/10.1007/s10548-016-0538-7

Article  PubMed  Google Scholar 

Wang X, Chen X, Cao C (2020a) Human emotion recognition by optimally fusing facial expression and speech feature. Signal Process Image Commun 84:115831

Article  Google Scholar 

Wang F, Wu S, Zhang W, Xu Z, Zhang Y, Wu C, Coleman S (2020b) Emotion recognition with convolutional neural network and EEG-based EFDMs. Neuropsychologia 146:107506

Article  PubMed  Google Scholar 

Wang Z-M, Chen Z-Y, Zhang J (2023) EEG emotion recognition based on PLV-rich-club dynamic brain function network. Appl Intell 53(14):17327–17345. https://doi.

留言 (0)

沒有登入
gif