Emotion recognition based on convolutional neural networks and heterogeneous bio-signal data sources
暂无分享,去创建一个
Haoran Xie | Di Zou | Wang Kay Ngai | Kee-Lee Chou | Haoran Xie | W. Ngai | D. Zou | K. Chou
[1] Olga Sourina,et al. Real-time EEG-based emotion monitoring using stable features , 2015, The Visual Computer.
[2] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[3] Enas Abdulhay,et al. Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS) , 2019, IEEE Access.
[4] Jianjun Lei,et al. Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges , 2020, IEEE Transactions on Emerging Topics in Computational Intelligence.
[5] Alex Martin,et al. Facial Emotion Recognition in Autism Spectrum Disorders: A Review of Behavioral and Neuroimaging Studies , 2010, Neuropsychology Review.
[6] P. Ekman,et al. The nature of emotion: Fundamental questions. , 1994 .
[7] Reinhard Pakrun,et al. EMOTIONS AND LEARNING 1 , 2013 .
[8] Takeo Kanade,et al. Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).
[9] Rama Chellappa,et al. FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).
[10] Muhammad Ghulam,et al. User emotion recognition from a larger pool of social network data using active learning , 2017, Multimedia Tools and Applications.
[11] Reda A. El-Khoribi,et al. EEG-Based Emotion Recognition using 3D Convolutional Neural Networks , 2018 .
[12] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[13] Yifei Lu,et al. Combining Eye Movements and EEG to Enhance Emotion Recognition , 2015, IJCAI.
[14] Manolis Mavrikis,et al. Affective learning: improving engagement and enhancing learning with affect-aware feedback , 2017, User Modeling and User-Adapted Interaction.
[15] Václav Snášel,et al. Neural Networks for Emotion Recognition Based on Eye Tracking Data , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.
[16] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[17] Roddy Cowie,et al. Describing the emotional states that are expressed in speech , 2003, Speech Commun..
[18] J. Russell. A circumplex model of affect. , 1980 .
[19] Thierry Pun,et al. Multimodal Emotion Recognition in Response to Videos , 2012, IEEE Transactions on Affective Computing.
[20] Jeremy R. Cooperstock,et al. Biosignals Analysis and its Application in a Performance Setting - Towards the Development of an Emotional-Imaging Generator , 2008, BIOSIGNALS.
[21] Manuchehr Soleimani,et al. Medical imaging and physiological modelling: linking physics and biology , 2009, Biomedical engineering online.
[22] Jiahui Pan,et al. Combining Facial Expressions and Electroencephalography to Enhance Emotion Recognition , 2019, Future Internet.
[23] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[24] Erik Cambria,et al. Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..
[25] K. Scherer,et al. Appraisal processes in emotion: Theory, methods, research. , 2001 .
[26] Mohammad H. Mahoor,et al. Going deeper in facial expression recognition using deep neural networks , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).
[27] Stefan Winkler,et al. Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning , 2015, ICMI.
[28] Tamás D. Gedeon,et al. Collecting Large, Richly Annotated Facial-Expression Databases from Movies , 2012, IEEE MultiMedia.
[29] Ioannis Pitas,et al. The eNTERFACE05 Audio-Visual Emotion Database , 2006, 22nd International Conference on Data Engineering Workshops (ICDEW'06).
[30] David J. Finch,et al. Managing emotions: A case study exploring the relationship between experiential learning, emotions, and student performance , 2015 .
[31] Xiao-Li Meng,et al. The Art of Data Augmentation , 2001 .
[32] Chalavadi Krishna Mohan,et al. Facial Expression Recognition Using Kinect Depth Sensor and Convolutional Neural Networks , 2014, 2014 13th International Conference on Machine Learning and Applications.
[33] R. Nagarajan,et al. Combining Spatial Filtering and Wavelet Transform for Classifying Human Emotions Using EEG Signals , 2011 .
[34] M. Bradley,et al. The pupil as a measure of emotional arousal and autonomic activation. , 2008, Psychophysiology.
[35] N. P. Reddy,et al. Facial expression (mood) recognition from facial images using committee neural networks , 2009, Biomedical engineering online.
[36] Li Li,et al. Emotion recognition based on the sample entropy of EEG. , 2014, Bio-medical materials and engineering.
[37] Haoran Xie,et al. Sentiment strength detection with a context-dependent lexicon-based convolutional neural network , 2020, Inf. Sci..
[38] Nitin Kumar,et al. Bispectral Analysis of EEG for Emotion Recognition , 2015, IHCI.
[39] Dawei Song,et al. Robust multi-source adaptation visual classification using supervised low-rank representation , 2017, Pattern Recognit..
[40] P. Lang. Behavioral treatment and bio-behavioral assessment: computer applications , 1980 .
[41] Jyoti Kumari,et al. Facial Expression Recognition: A Survey , 2015 .
[42] Olga Sourina,et al. A Fractal-based Algorithm of Emotion Recognition from EEG using Arousal-Valence Model , 2011, BIOSIGNALS.
[43] Jakob de Lemos,et al. Measuring emotions using eye tracking , 2008 .
[44] Samit Bhattacharya,et al. Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset , 2017, AAAI.
[45] Hong Zhang,et al. Facial expression recognition via learning deep sparse autoencoders , 2018, Neurocomputing.
[46] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[47] Raymond Y. K. Lau,et al. Universal affective model for Readers' emotion classification over short texts , 2018, Expert Syst. Appl..
[48] Seyed Kamaledin Setarehdan,et al. Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods , 2018, International Clinical Neuroscience Journal.
[49] Reza Rostami,et al. Classifying depression patients and normal subjects using machine learning techniques , 2011, 2011 19th Iranian Conference on Electrical Engineering.
[50] Takeo Kanade,et al. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.
[51] Rui Li,et al. Classification of Five Emotions from EEG and Eye Movement Signals: Complementary Representation Properties , 2019, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER).
[52] Erik Cambria,et al. The Hourglass Model Revisited , 2020, IEEE Intelligent Systems.
[53] Suhua Zhang,et al. An approach to EEG-based emotion recognition using combined feature extraction method , 2016, Neuroscience Letters.
[54] Jun Yu,et al. Deep Neural Networks with Relativity Learning for facial expression recognition , 2016, 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).
[55] P. Lang. International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .
[56] Shiting Wen,et al. L1-norm locally linear representation regularization multi-source adaptation learning , 2015, Neural Networks.
[57] Soonja Yeom,et al. Facial expression recognition using a multi-level convolutional neural network , 2018, ICPR 2018.
[58] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[59] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[60] Seyed Kamaledin Setarehdan,et al. A NOVEL METHOD OF EEG-BASED EMOTION RECOGNITION USING NONLINEAR FEATURES VARIABILITY AND DEMPSTER–SHAFER THEORY , 2018, Biomedical Engineering: Applications, Basis and Communications.
[61] Shiting Wen,et al. Multi-source adaptation joint kernel sparse representation for visual classification , 2016, Neural Networks.
[62] Oleg V. Komogortsev,et al. Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors , 2010, IEEE Transactions on Biomedical Engineering.
[63] Soo-Hyung Kim,et al. Emotion Recognition by Integrating Eye Movement Analysis and Facial Expression Model , 2019, ICMLSC 2019.
[64] Paris Smaragdis,et al. Deep learning for monaural speech separation , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[65] Bao-Liang Lu,et al. Classification of Five Emotions from EEG and Eye Movement Signals: Discrimination Ability and Stability over Time , 2019, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER).
[66] Daniel S. Kermany,et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.
[67] Kai Keng Ang,et al. ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[68] Sunil Semwal,et al. A Review of Eeg Sensors used for Data Acquisition , 2012 .
[69] Qingmei Yao,et al. Multi-Sensory Emotion Recognition with Speech and Facial Expression , 2014 .
[70] Hao Guo,et al. Novel Algorithm for Measuring the Complexity of Electroencephalographic Signals in Emotion Recognition , 2017 .
[71] Bao-Liang Lu,et al. Emotional state classification from EEG data using machine learning approach , 2014, Neurocomputing.
[72] David F. Nichols,et al. Are Electrode Caps Worth the Investment? An Evaluation of EEG Methods in Undergraduate Neuroscience Laboratory Courses and Research. , 2016, Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience.
[73] Jiahui Pan,et al. Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition , 2017, Comput. Intell. Neurosci..
[74] Amos J. Storkey,et al. Data Augmentation Generative Adversarial Networks , 2017, ICLR 2018.
[75] Alberto J. Molina-Cantero,et al. Emotions Detection based on a Single-electrode EEG Device , 2017, PhyCS.