Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278

PubMed  Google Scholar 

Afshani F, Shalbaf A, Shalbaf R, Sleigh J (2019) Frontal–temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia. Cogn Neurodyn 13(6):531–540

PubMed  PubMed Central  Google Scholar 

Akar SA, Kara S, Agambayev S, Bilgiç V (2015) Nonlinear analysis of EEGs of patients with major depression during different emotional states. Comput Biol Med 67:49–60

Google Scholar 

Alarcao SM, Fonseca MJ (2017) Emotions recognition using EEG signals: a survey. IEEE Trans Affect Comput 10(3):374–393

Google Scholar 

Allen JJ, Keune PM, Schönenberg M, Nusslock R (2018) Frontal EEG alpha asymmetry and emotion: From neural underpinnings and methodological considerations to psychopathology and social cognition. Psychophysiology 55(1):e13028

Google Scholar 

Atkinson J, Campos D (2016) Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl 47:35–41

Google Scholar 

Astolfi L, Cincotti F, Mattia D, Marciani MG, Baccala LA et al (2007) Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp 28(2):143–157

PubMed  Google Scholar 

Astolfi L, Fallani FDV, Cincotti F, Mattia D, Marciani MG et al (2008) Estimation of effective and functional cortical connectivity from neuroelectric and hemodynamic recordings. IEEE Trans Neural Syst Rehabil Eng 17(3):224–233

PubMed  PubMed Central  Google Scholar 

Başar E (2012) A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology. Int J Psychophysiol 86:1–24

PubMed  Google Scholar 

Chaudhary S, Taran S, Bajaj V, Sengur A (2019) Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens J 19(12):4494–4500

Google Scholar 

Craik A, He A, Contreras-Vidal JLA (2019) Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng 16(3):031001

PubMed  Google Scholar 

Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7(3–4):197–387

Google Scholar 

Ekman P (1992) Are there basic emotions? Psychol Rev 99:550–553

CAS  PubMed  Google Scholar 

Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed 161:1–3

PubMed  Google Scholar 

Ghahari S, Salehi F, Farahani N, Coben R, Nasrabadi AM (2020) Representing temporal network based on dDTF of EEG signals in children with autism and healthy children. Biomed Signal Process Control 62:102139

Google Scholar 

Guo Y, Liu A, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48

Google Scholar 

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

Iacoviello D, Petracca A, Spezialetti M, Placidi G (2015) A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Comput Methods Programs Biomed 122(3):293–303

PubMed  Google Scholar 

Jaušovec N, Jaušovec K, Gerlič I (2011) Differences in event-related and induced EEG patterns in the theta and alpha frequency bands related to human emotional intelligence. Neurosci Lett 311:93–96

Google Scholar 

Jie X, Cao R, Li L (2014) Emotion recognition based on the sample entropy of EEG. Bio-Med Mater Eng 24(1):1185–1192

Google Scholar 

Klimesch W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci 16:606–617

PubMed  PubMed Central  Google Scholar 

Koelstra S, Muhl C, Soleymani M, Lee JS, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2011) DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18–31

Google Scholar 

Kohan MD, Nasrabadi AM, Shamsollahi MB, Sharifi A (2020) EEG/PPG effective connectivity fusion for analyzing deception in interview. Signal Image Video Process 14:907–914

Google Scholar 

Korzeniewska A, Mańczak M, Kamiński M, Blinowska KJ, Kasicki S (2003) Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J Neurosci Methods 125(1–2):195–207

PubMed  Google Scholar 

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

Kus R, Kaminski M, Blinowska KJ (2004) Determination of EEG activity propagation: pair-wise versus multichannel estimate. IEEE Trans Biomed Eng 51(9):1501–1510

PubMed  Google Scholar 

Lee YY, Hsieh S (2014) Classifying different emotional states by means of EEG-based functional connectivity patterns. PLOS ONE 9(4):e95415

PubMed  PubMed Central  Google Scholar 

Li Y, Cao D, Wei L, Tang Y, Wang J (2015) Abnormal functional connectivity of EEG gamma band in patients with depression during emotional face processing. Clin Neurophysiol 126(11):2078–2089

PubMed  Google Scholar 

Li P, Liu H, Si Y, Li C, Li F, Zhu X, Huang X, Zeng Y, Yao D, Zhang Y, Xu P (2019) EEG based emotion recognition by combining functional connectivity network and local activations. IEEE Trans Biomed Eng 66(10):2869–2881

PubMed  Google Scholar 

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

PubMed  Google Scholar 

Liu J, Meng H, Li M, Zhang F, Qin R, Nandi AK (2018) Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction. Concurr Comput Pract Exp 30(23):e4446

Google Scholar 

Maghsoudi A, Shalbaf A (2020) Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals. J Biomed Phys Eng

Mohseni HR, Maghsoudi A, Kadbi MH, Hashemi J, Ashourvan A (2006) Automatc detection of epileptic seizure using time-frequency distributions. In 2006 IET 3rd International conference on advances in medical, signal and information processing-MEDSIP 2006, pp 1–4

Mohseni HR, Maghsoudi A, Shamsollahi MB (2006) Seizure detection in EEG signals: a comparison of different approaches. In: 2006 International conference of the IEEE engineering in medicine and biology society, pp 6724–6727

Mullen T (2010) Source information flow toolbox (SIFT). Swartz Center for Computational Neuroscience, California, San Diego

Nie D, Wang XW, Shi LC, Lu BL (2011) EEG-based emotion recognition during watching movies. In: 2011 5th international IEEE/EMBS conference on neural engineering, pp 667–670

Rouzbahani M, Nasrabadi AM (2017) Effective connectivity estimation based on emotion EEG signal by granger causality and directed transfer function. Adv Cogn Sci 19:1–18

Google Scholar 

Rottenberg J, Ray RD, Gross JJ (2007) Emotion elicitation using films. In: Handbook of emotion elicitation and assessment, series in affective science. Oxford University Press, pp 9–28

Roy Y, Banville H, Albuquerque I, Gramfort A, Falk TH, Faubert J (2019) Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng 16(5):051001

PubMed  Google Scholar 

Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161

Google Scholar 

Sadati N, Mohseni HR, Maghsoudi A (2006) Epileptic seizure detection using neural fuzzy networks. In: 2006 IEEE international conference on fuzzy systems, pp 596–600

Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A (2020) Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn 15:239–252

PubMed  Google Scholar 

Saeedi M, Saeedi A, Maghsoudi A (2020) Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals. Phys Eng Sci Med 43:1007–1018

PubMed  Google Scholar 

Schaefer A, Nils F, Sanchez X, Philippot P (2010) Assessing the effectiveness of a large database of emotion-eliciting films: a new tool for emotion researchers. Cogn Emot 24:1153–1172

Google Scholar 

Shahabi H, Moghimi S (2016) Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity. Comput Hum Behav 58:231–239

Google Scholar 

Shalbaf A, Shalbaf R, Saffar M, Sleigh J (2020) Monitoring the level of hypnosis using a hierarchical SVM system. J Clin Monit Comput 34(2):331–338

PubMed  Google Scholar 

Shalbaf A, Maghsoudi A (2020) Mental arithmetic task recognition using effective connectivity and hierarchical feature selection from EEG Signals. Basic Clin Neurosci

Shen F, Dai G, Lin G, Zhang J, Kong W, Zeng H (2020) EEG-based emotion recognition using 4D convolutional recurrent neural network. Cogn Neurodyn 14(6):815–828

PubMed  PubMed Central  Google Scholar 

Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45:427–437

Google Scholar 

Soleymani M, Pantic M (2013) Multimedia implicit tagging using EEG signals. In: IEEE international conference on multimedia and expo (ICME), pp 1–6

Soroush MZ, Maghooli K, Setarehdan SK, Nasrabadi AM (2018) A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory. Behav Brain Funct 14(1):17

Google Scholar 

Soroush MZ, Maghooli K, Setarehdan SK, Nasrabadi AM (2019) Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory. Med Hypotheses 127:34–45

Google Scholar 

Soroush MZ, Maghooli K, Setarehdan SK, Nasrabadi AM (2020) Emotion recognition using EEG phase space dynamics and Poincare intersections. Biomed Signal Process Control 59:101918

Google Scholar 

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

Tafreshi TF, Daliri MR, Ghodousi M (2019) Functional and effective connectivity based features of EEG signals for object recognition. Cogn Neurodyn 13(6):555–566

PubMed  PubMed Central  Google Scholar 

Thammasan N, Moriyama K, Fukui KI, Numao M (2017) Familiarity effects in EEG-based emotion recognition. Brain Inf 4(1):39–50

Google Scholar 

Wang Z, Tong Y, Heng X (2019) Phase-locking value-based graph convolutional neural networks for emotion recognition. IEEE Access 7:93711–93722

Google Scholar 

Wu X, Zheng WL, Lu BL (2019) Identifying functional brain connectivity patterns for EEG-based emotion recognition. In: 2019 9th international IEEE/EMBS conference on neural engineering (NER) 2019 Mar 20, pp 235–238

Xiaofen X, Li Z, Xu T, Shu L, Hu B, Xu X (2019) SAE+ LSTM: a new framework for emotion recognition from multi-channel EEG. Front Neurorobot 13:37

Google Scholar 

Yang YX, Gao ZK, Wang XM, Li YL, Han JW, Marwan N, Kurths J (2018) A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG. Chaos 28(8):085724

PubMed  Google Scholar 

Yang H, Han J, Min K (2019) A multi-column CNN model for emotion recognition from EEG signals. Sensors 19:4736

PubMed Central  Google Scholar 

Yuvaraj R, Murugappan M (2016) Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients. Cogn Neurodyn 10(3):225–234

CAS  PubMed  PubMed Central  Google Scholar 

Zeng H, Wu Z, Zhang J, Yang C, Zhang H, Dai G, Kong W (2019) EEG emotion classification using an improved SincNet-based deep learning model. Brain Sci 9(11):326

PubMed Central  Google Scholar 

Zhang Y, Ji X, Zhang S (2016) An approach to EEG-based emotion recognition using combined feature extraction method. Neurosci Lett 633:152–157

CAS  PubMed  Google Scholar 

Zhang J, Zhao S, Huang W, Hu S (2017) Brain effective connectivity analysis from EEG for positive and negative emotion. In: International conference on neural information processing, pp 851–857

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

Google Scholar 

Zheng WL, Zhu JY, Lu BL (2017) Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans Affect Comput 10:417–429

Google Scholar 

Zisserman SK (2014) Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556.