Emotion recognition system using short-term monitoring of physiological signals

Pattern Classification

Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,525 · see citing works

A Tutorial on Support Vector Machines for Pattern Recognition

Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works

Affective Computing

Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works

An overview of statistical learning theory

Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,305 · see citing works

Human-Computer Interaction

Carayannis, E. G. (2013) · Springer eBooks · doi:10.1007/978-1-4614-3858-8_100431 · Cited by 4,818 · see citing works

Pattern Classification

Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,740 · see citing works

Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI

Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002) · NeuroImage · doi:10.1006/nimg.2002.1087 · Cited by 3,502 · see citing works

Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999

Nazuno, J. F. (2000) · Computación y Sistemas · Cited by 3,185 · see citing works

Emotion recognition in human-computer interaction

Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001) · IEEE Signal Processing Magazine · doi:10.1109/79.911197 · Cited by 2,603 · see citing works

Pattern Classification

Duda, R. O., Hart, P. E. 1., & Stork, D. G. C. 2. /. J. (2001) · Cited by 2,469 · see citing works

Toward machine emotional intelligence: analysis of affective physiological state

Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334 · see citing works

Support vector machines for histogram-based image classification

Chapelle, O., Haffner, P., & Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788646 · Cited by 1,468 · see citing works

Psychophysiology -- Human Behavior and Physiological Response

McGuigan, F. J., & Andreassi, J. L. (1981) · The American Journal of Psychology · doi:10.2307/1422751 · Cited by 979 · see citing works

Single-layer learning revisited: a stepwise procedure for building and training a neural network

Knerr, S., Personnaz, L., & Dreyfus, G. (1990) · Springer eBooks · doi:10.1007/978-3-642-76153-9_5 · Cited by 837 · see citing works

Electrodermal Activity

Boucsein, W. (1992) · doi:10.1007/978-1-4757-5093-5 · Cited by 818 · see citing works

An Efficient Algorithm for Spectral Analysis of Heart Rate Variability

Berger, R. D., AKSELROD, S., Gordon, D. H., & Cohen, R. J. (1986) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1986.325789 · Cited by 808 · see citing works

The effects of emotions on short-term power spectrum analysis of heart rate variability

McCraty, R., Atkinson, M., Tiller, W. A., Rein, G., & Watkins, A. D. (1995) · The American Journal of Cardiology · doi:10.1016/s0002-9149(99)80309-9 · Cited by 632 · see citing works

Autonomic balance revisited: Panic anxiety and heart rate variability

Friedman, B. H., & Thayer, J. F. (1998) · Journal of Psychosomatic Research · doi:10.1016/s0022-3999(97)00202-x · Cited by 616 · cited 1× · see citing works

On amplitude and frequency demodulation using energy operators

Maragos, P. A., Kaiser, J. F., & Quatieri, T. F. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.212729 · Cited by 541 · see citing works

Biological Engineering

(2015) · Biological Engineering · doi:10.13031/issn.1934-2799 · Cited by 274 · see citing works

Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication

Tefas, A., Kotropoulos, C. L., & Pitas, I. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.935847 · Cited by 219 · see citing works

Finite sample criteria for autoregressive order selection

BROERSEN, P. M. T. (2000) · IEEE Transactions on Signal Processing · doi:10.1109/78.887047 · Cited by 131 · see citing works

Automatic recognition and understanding of spoken language - a first step toward natural human-machine communication

Juang, B.-H., & Furui, S. (2000) · Proceedings of the IEEE · doi:10.1109/5.880077 · Cited by 130 · see citing works

The effect of expressing anger on cardiovascular reactivity and facial blood flow in Chinese and Caucasians

Drummond, P. D., & Quah, S. H. (2001) · Psychophysiology · doi:10.1111/1469-8986.3820190 · Cited by 128 · see citing works

Digital processing of affective signals

Healey, J., & Picard, R. W. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.679699 · Cited by 122 · see citing works

The Emotion Mouse

Ark, W., Dryer, D. C., & Lu, D. J. (1999) · International Conference on Human-Computer Interaction · Cited by 114 · see citing works

Ethological modeling and architecture for an entertainment robot

Arkin, R., Fujita, M., Takagi, T., & Hasegawa, R. (2001) · Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164) · doi:10.1109/robot.2001.932592 · Cited by 93 · see citing works

Psychophysiology human behavior and physiological response

Andreassi, J. L. (2007) · Cited by 77 · see citing works

Facts and fiction in spectral analysis

BROERSEN, P. M. T. (2000) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/19.863921 · Cited by 62 · see citing works

Room service, AI-style

Hirsh, H., Coen, M. H., Mozer, M., Hasha, R., & Flanagan, J. (1999) · IEEE Intelligent Systems · doi:10.1109/5254.757622 · Cited by 56 · see citing works

Development of a skin temperature measuring system for non-contact stress evaluation

Kataoka, H., Kano, H., Yoshida, H., Saijo, A., Yasuda, M., & OSUMI, M. (2002) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.1998.745598 · Cited by 56 · see citing works

Signal processing for recognition of human frustration

Fernandez, R., & Picard, R. W. (1998) · Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181) · doi:10.1109/icassp.1998.679705 · Cited by 50 · see citing works

An International Forum for Space AI and Robotics

Doyle, R. J., Montemerlo, M. D., Nakatani, I., Piedbœuf, J.-C., & Pütz, P. (1999) · IEEE Intelligent Systems · Cited by 29 · see citing works

Automatic speech recognition and understanding : A first step toward natural human-machine communication

Bung, H. (2000) · Medical Entomology and Zoology · Cited by 26 · see citing works

Analysis of skin-temperature variability compared to variability of blood pressure and heart rate

Barnea, O., & Shusterman, V. (2002) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.1995.579449 · Cited by 17 · see citing works

Effects of record length selection on the accuracy of spectral estimates of heart rate variability: a simulation study

Pinna, G. D., Maestri, R., & Sanarico, M. (1996) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.503184 · Cited by 16 · see citing works

Time domain heart rate variability index for assessment of dynamic stress

Wang, F., Sagawa, K., & Inooka, H. (2002) · Computers in cardiology · doi:10.1109/cic.1998.731734 · Cited by 13 · see citing works

A novel robust index to assess beat-to-beat variability in heart rate time-series analysis

Garcia-Gonzalez, M. A., & Pallas-Areny, R. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.923779 · Cited by 10 · see citing works

A novel approach to analyze the result of polygraph

Jiang, L., Qing, Z., & Wenyuan, W. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2000.884436 · Cited by 7 · see citing works

Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · see citing works

Emotion recognition based on physiological changes in music listening

Kim, J., & André, E. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.26 · Cited by 1,079 · see citing works

EEG-Based Emotion Recognition in Music Listening

Lin, Y., Wang, C., Jung, T., Wu, T.-L., Jeng, S., Duann, J., & Chen, J. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2048568 · Cited by 1,022 · see citing works

Stretchable, Transparent, Ultrasensitive, and Patchable Strain Sensor for Human–Machine Interfaces Comprising a Nanohybrid of Carbon Nanotubes and Conductive Elastomers

Roh, E., Hwang, B.-U., Kim, D., Kim, B.-Y., & Lee, N.-E. (2015) · ACS Nano · doi:10.1021/acsnano.5b01613 · Cited by 927 · see citing works

Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review

Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806 · see citing works

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Schuller, B. W., Batliner, A. M., Steidl, S., & Seppi, D. (2011) · Speech Communication · doi:10.1016/j.specom.2011.01.011 · Cited by 765 · see citing works

Review on Psychological Stress Detection Using Biosignals

Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2927337 · Cited by 764 · see citing works

Emotional state classification from EEG data using machine learning approach

Wang, X.-W., Nie, D., & Lu, B.-L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.06.046 · Cited by 755 · see citing works

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis, A., Kaklauskas, A., & Bučinskas, V. (2020) · Sensors · doi:10.3390/s20030592 · Cited by 596 · see citing works

Ecg pattern analysis for emotion detection

Agrafioti, F., Hatzinakos, D., & Anderson, A. K. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.28 · Cited by 476 · see citing works

Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review

Houssein, E. H., Hammad, A. S. A., & Ali, A. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07292-4 · Cited by 449 · see citing works

Classification of human emotion from EEG using discrete wavelet transform

Murugappan, M., Ramachandran, N., & Sazali, Y. (2010) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2010.34054 · Cited by 435 · see citing works

Cognitive and affective processes for learning science in immersive virtual reality

Parong, J. A., & Mayer, R. E. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12482 · Cited by 426 · see citing works

Ambient intelligence

Sadri, F. (2011) · ACM Computing Surveys · doi:10.1145/1978802.1978815 · Cited by 365 · see citing works

EEG-based workers' stress recognition at construction sites

Jebelli, H., Hwang, S., & Lee, S. H. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.05.027 · Cited by 350 · see citing works

Physiological signals based human emotion Recognition: a review

Jerritta, S., Murugappan, M., Nagarajan, R., & Khairunizam, W. (2011) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa.2011.5759912 · Cited by 349 · see citing works

Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach

Katsis, C. D., Katertsidis, N. S., Ganiatsas, G., & Fotiadis, D. I. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.918624 · Cited by 332 · see citing works

A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

Bota, P. J., Wang, C., Fred, A. L. N., & Silva, H. (2019) · IEEE Access · doi:10.1109/access.2019.2944001 · Cited by 329 · see citing works

Self-Regulation of Breathing as a Primary Treatment for Anxiety

Jerath, R., Crawford, M. W., Barnes, V. A., & Harden, K. (2015) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-015-9279-8 · Cited by 316 · see citing works

An empirical study of machine learning techniques for affect recognition in human–robot interaction

Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0025-y · Cited by 313 · see citing works

A Pervasive Approach to EEG‐Based Depression Detection

Cai, H., Han, J., Chen, Y.-F., Sha, X., Wang, Z., Hu, B., Yang, J., Feng, L., Ding, Z., Chen, Y., et al. (2018) · Complexity · doi:10.1155/2018/5238028 · Cited by 302 · see citing works

Learning deep physiological models of affect

Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2247823 · Cited by 287 · see citing works

EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines

Wang, X., Nie, D., & Lu, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24955-6_87 · Cited by 277 · see citing works

Emotion recognition using brain activity

Horlings, R., Datcu, D., & Rothkrantz, L. J. M. (2008) · International Conference on Computer Systems and Technologies (CompSysTech) · doi:10.1145/1500879.1500888 · Cited by 269 · see citing works

Wearable-Based Affect Recognition—A Review

Schmidt, P., Reiss, A., Dürichen, R., & Laerhoven, K. V. (2019) · Sensors · doi:10.3390/s19194079 · Cited by 267 · see citing works

Dynamic Difficulty Adjustment in Computer Games Through Real-Time Anxiety-Based Affective Feedback

Liu, C., Agrawal, P., Sarkar, N., & Chen, S. (2009) · International Journal of Human-Computer Interaction · doi:10.1080/10447310902963944 · Cited by 259 · see citing works

Automatic ECG-Based Emotion Recognition in Music Listening

Hsu, Y., Wang, J.-S., Chiang, W.-C., & Hung, C.-H. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2781732 · Cited by 258 · see citing works

The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition

Valenza, G., Lanatà, A., & Scilingo, E. P. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.30 · Cited by 250 · see citing works

Toward Emotion Aware Computing: An Integrated Approach Using Multichannel Neurophysiological Recordings and Affective Visual Stimuli

Frantzidιs, C. A., Bratsas, C., Papadelis, C. L., Konstantinidis, E. I., Pappas, C., & Bamidis, P. D. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2010.2041553 · Cited by 250 · see citing works

Affective state estimation for human–robot Interaction

Kulic, D., & Croft, E. A. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.904899 · Cited by 230 · see citing works

Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

Nardelli, M., Valenza, G., Greco, A., Lanatà, A., & Scilingo, E. P. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2432810 · Cited by 219 · see citing works

A survey of machine learning techniques in physiology based mental stress detection systems

Panicker, S., & Gayathri, P. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.01.004 · Cited by 217 · see citing works

Comparison of different feature extraction methods for EEG-based emotion recognition

Nawaz, R., Cheah, K. H., Nisar, H., & Yap, V. V. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.04.005 · Cited by 213 · see citing works

Stress Detection Using Wearable Physiological and Sociometric Sensors

Mozos, Ó. M., Săndulescu, V., Andrews, S., Ellis, D. A., Bellotto, N., Dobrescu, R., & Vicente, J. M. F. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065716500416 · Cited by 205 · see citing works

EEG-based emotion recognition using simple recurrent units network and ensemble learning

Chen, W., Chen, L., Song, Z., Xiao-guang, L., & Li, D. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101756 · Cited by 195 · see citing works

Nanoparticle Based Curve Arrays for Multirecognition Flexible Electronics

Su, M., Li, F., Chen, S., Huang, Z., Qin, M., Li, W., Zhang, X., & Song, Y. (2015) · Advanced Materials · doi:10.1002/adma.201504759 · Cited by 188 · see citing works

Behavioural and physiological assessment of positive and negative emotion in sheep

Reefmann, N., Wechsler, B., & Gygax, L. (2009) · Animal Behaviour · doi:10.1016/j.anbehav.2009.06.015 · Cited by 175 · see citing works

Heart rate variability monitoring for emotion and disorders of emotion

Zhu, J., Ji, L., & Liu, C. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab1887 · Cited by 171 · see citing works

A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing

Novak, D., Mihelj, M., & Munih, M. (2012) · Interacting with Computers · doi:10.1016/j.intcom.2012.04.003 · Cited by 163 · see citing works

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions

Jang, E.-H., Park, B.-J., Park, M.-S., Kim, S.-H., & Sohn, J.-H. (2015) · Journal of Physiological Anthropology · doi:10.1186/s40101-015-0063-5 · Cited by 159 · see citing works

EEG-Based Emotion Classification Using Spiking Neural Networks

Luo, Y., Fu, Q., Xie, J., Qin, Y., Wu, G., Liu, J., Jiang, F., Cao, Y., & Ding, X. (2020) · IEEE Access · doi:10.1109/access.2020.2978163 · Cited by 159 · see citing works

Wearable Devices for Precision Medicine and Health State Monitoring

Jeong, I. C., Bychkov, D., & Searson, P. C. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2871638 · Cited by 155 · see citing works

Experimental investigation about effect of emotion state on people's thermal comfort

Wang, H., & Liu, L. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.109789 · Cited by 153 · see citing works

Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT)

Murugappan, M., & Murugappan, S. (2013) · 2013 IEEE 9th International Colloquium on Signal Processing and its Applications · doi:10.1109/cspa.2013.6530058 · Cited by 152 · see citing works

A Flexible and Wearable Human Stress Monitoring Patch

Yoon, S., Sim, J. K., & Cho, Y.-H. (2016) · Scientific Reports · doi:10.1038/srep23468 · Cited by 151 · see citing works

A Systematic Review on Multimodal Emotion Recognition: Building Blocks, Current State, Applications, and Challenges

Kalateh, S., Estrada-Jimenez, L. A., Nikghadam-Hojjati, S., & Barata, J. (2024) · IEEE Access · doi:10.1109/access.2024.3430850 · Cited by 151 · see citing works

Emotion Recognition Based on Multi-Variant Correlation of Physiological Signals

Wen, W., Liu, G., Cheng, N., Wei, J., Shangguan, P., & Huang, W. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2327617 · Cited by 150 · see citing works

Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State

Hosseini, S. A., & Khalilzadeh, M. A. (2010) · International Conference on Biomedical Engineering and Computer Science · doi:10.1109/icbecs.2010.5462520 · Cited by 149 · see citing works

Computational approaches for emotion detection in text

Binali, H., Wu, C., & Potdar, V. M. (2010) · IEEE International Conference on Digital Ecosystems and Technologies · doi:10.1109/dest.2010.5610650 · Cited by 146 · see citing works

Application of Wearable Biosensors to Construction Sites. I: Assessing Workers’ Stress

Jebelli, H., Choi, B., & Lee, S. H. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001729 · Cited by 146 · see citing works

Bimodal Emotion Recognition using Speech and Physiological Changes

Kim, J. (2007) · doi:10.5772/4754 · Cited by 145 · see citing works

Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG

Khalili, Z., & Moradi, M. H. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178854 · Cited by 145 · see citing works

Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm

Hu, B., Li, X., Sun, S., & Ratcliffe, M. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2616395 · Cited by 143 · see citing works

Emotion recognition based on EEG features in movie clips with channel selection

Özerdem, M. S., & Polat, H. (2017) · Brain Informatics · doi:10.1007/s40708-017-0069-3 · Cited by 136 · see citing works

Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework

Nakisa, B., Rastgoo, M. N., Rakotonirainy, A., Maire, F., & Chandran, V. (2018) · IEEE Access · doi:10.1109/access.2018.2868361 · Cited by 135 · see citing works

Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method

Li, J., Jin, Y., Lu, S., Wu, W. L., & Wang, P. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110259 · Cited by 135 · see citing works

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks

Movahedi, F., Coyle, J. L., & Sejdić, E. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2727218 · Cited by 132 · see citing works

Detecting Naturalistic Expressions of Nonbasic Affect Using Physiological Signals

AlZoubi, O., D’Mello, S. K., & Calvo, R. A. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.4 · Cited by 131 · see citing works

Emotional Self-Regulation of Individuals with Autism Spectrum Disorders: Smartwatches for Monitoring and Interaction

Torrado, J. C., Gomez, J., & Montoro, G. (2017) · Sensors · doi:10.3390/s17061359 · Cited by 130 · see citing works

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

Selvaraj, J., Murugappan, M., Khairunizam, W., & Yaacob, S. (2013) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-12-44 · Cited by 130 · see citing works

Wearable Emotion Recognition System based on GSR and PPG Signals

Udovičić, G., Đerek, J., Russo, M., & Sikora, M. (2017) · International Workshop on Multimedia for Personal Health and Health Care (MMHealth) · doi:10.1145/3132635.3132641 · Cited by 129 · see citing works

Individual identification via electrocardiogram analysis

Fratini, A., Sansone, M., Bifulco, P., & Cesarelli, M. (2015) · BioMedical Engineering OnLine · doi:10.1186/s12938-015-0072-y · Cited by 126 · see citing works

Assessment of construction workers’ perceived risk using physiological data from wearable sensors: A machine learning approach

Lee, B. G., Choi, B., Jebelli, H., & Lee, S. H. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102824 · Cited by 125 · see citing works

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

Lin, W., & Li, C. (2023) · Applied Sciences · doi:10.3390/app13042573 · Cited by 124 · see citing works

The Psychophysiology Primer: A Guide to Methods and a Broad Review with a Focus on Human–Computer Interaction

Cowley, B. U., Filetti, M., Lukander, K., Torniainen, J., Henelius, A., Ahonen, L., Barral, O., Kosunen, I., Valtonen, T., Huotilainen, M., et al. (2016) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000065 · Cited by 123 · see citing works

Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine

Guo, H.-W., Huang, Y.-S., Lin, C., Chien, J.-C., Haraikawa, K., & Shieh, J.-S. (2016) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2016.40 · Cited by 123 · see citing works

Human-building interaction under various indoor temperatures through neural-signal electroencephalogram (EEG) methods

Shan, X. J., Yang, E., Zhou, J. Z., & Chang, V. W.-C. (2017) · Building and Environment · doi:10.1016/j.buildenv.2017.12.004 · Cited by 122 · see citing works

An affective computing approach to physiological emotion specificity: Toward subject‐independent and stimulus‐independent classification of film‐induced emotions

Kolodyazhniy, V., Kreibig, S. D., Gross, J. J., Roth, W. T., & Wilhelm, F. H. (2011) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01170.x · Cited by 122 · see citing works

A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition

Wang, J.-S., Lin, C., & Yang, Y.-T. C. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.10.047 · Cited by 121 · see citing works

EEG-based human emotion recognition using entropy as a feature extraction measure

Patel, P., Raghunandan, R., & Annavarapu, R. N. (2021) · Brain Informatics · doi:10.1186/s40708-021-00141-5 · Cited by 120 · see citing works

Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach

Kanjo, E., Younis, E. M. G., & Sherkat, N. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.05.005 · Cited by 118 · see citing works

Emotion Recognition from Surface EMG Signal Using Wavelet Transform and Neural Network

Cheng, B., & Liu, G. (2008) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2008.670 · Cited by 118 · see citing works

GLFANet: A global to local feature aggregation network for EEG emotion recognition

Liu, S., Zhao, Y., An, Y., Zhao, J., Wang, S., & Yan, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104799 · Cited by 116 · see citing works

Music mood and human emotion recognition based on physiological signals: a systematic review

Chaturvedi, V., Kaur, A. B., Varshney, V., Garg, A., Chhabra, G. S., & Kumar, M. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00786-6 · Cited by 115 · see citing works

Emotion Recognition Using Physiological Signals

Li, L., & Chen, J. (2006) · Lecture notes in computer science · doi:10.1007/11941354_44 · Cited by 112 · see citing works

An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders

Katsis, C. D., Katertsidis, N. S., & Fotiadis, D. I. (2010) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2010.12.001 · Cited by 110 · see citing works

A Review on EEG Signals Based Emotion Recognition

Soroush, M. Z., Maghooli, K., Setarehdan, S. K., & Nasrabadi, A. M. (2017) · International Clinical Neuroscience Journal · doi:10.15171/icnj.2017.01 · Cited by 108 · see citing works

Towards emotion recognition from electroencephalographic signals

Schaaff, K., & Schultz, T. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349316 · Cited by 107 · see citing works

Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection

Larradet, F., Niewiadomski, R., Barresi, G., Caldwell, D. G., & Mattos, L. S. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01111 · Cited by 105 · see citing works

Affective Computing and Intelligent Interaction

Picard, R. W., Tao, J., Tan, T., & 2005, A. (2005) · Lecture notes in computer science · doi:10.1007/11573548 · Cited by 105 · see citing works

Can you feel what you do not see? Using internal feedback to detect briefly presented emotional stimuli

Bornemann, B., Winkielman, P., & der Meer, E. V. (2012) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2011.04.007 · Cited by 104 · see citing works

Differences of Heart Rate Variability Between Happiness and Sadness Emotion States: A Pilot Study

Shi, H., Yang, L., Zhao, L., Su, Z., Mao, X., Li, Z., & Liu, C. (2017) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-017-0238-0 · Cited by 104 · see citing works

Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature

Khan, M. M., Ward, R. D., & Ingleby, M. (2009) · ACM Transactions on Applied Perception · doi:10.1145/1462055.1462061 · Cited by 104 · see citing works

On the analysis of EEG power, frequency and asymmetry in Parkinson’s disease during emotion processing

Yuvaraj, R., Murugappan, M., Ibrahim, N. M., Iqbal, M. Z., Sundaraj, K., Mohamad, K., Palaniappan, R., Mesquita, E. M., & Satiyan, M. (2014) · Behavioral and Brain Functions · doi:10.1186/1744-9081-10-12 · Cited by 103 · see citing works

Time-Frequency Analysis of EEG Signals for Human Emotion Detection

Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Desa, H., & Zunaidi, I. (2008) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-540-69139-6_68 · Cited by 102 · see citing works

Intimate Heartbeats: Opportunities for Affective Communication Technology

Janssen, J. H., Bailenson, J. N., IJsselsteijn, W. A., & Westerink, J. H. D. M. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.13 · Cited by 102 · see citing works

Automatic Emotion Recognition Using Temporal Multimodal Deep Learning

Nakisa, B., Rastgoo, M. N., Rakotonirainy, A., Maire, F., & Chandran, V. (2020) · IEEE Access · doi:10.1109/access.2020.3027026 · Cited by 101 · see citing works

Application of Wearable Biosensors to Construction Sites. II: Assessing Workers’ Physical Demand

Jebelli, H., Choi, B., & Lee, S. H. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001710 · Cited by 99 · see citing works

Detection of Psychological Stress Using a Hyperspectral Imaging Technique

Chen, T., Yuen, P. W. T., Richardson, M. A., Liu, G., & She, Z. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2362513 · Cited by 97 · see citing works

Physiological expression of emotional reactions in sheep

Reefmann, N., Kaszàs, F. B., Wechsler, B., & Gygax, L. (2009) · Physiology & Behavior · doi:10.1016/j.physbeh.2009.05.017 · Cited by 95 · see citing works

Biometric applications in education

Hernández-de-Menéndez, M., Morales-Menéndez, R., Escobar, C. A., & Arinez, J. F. (2021) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-021-00760-6 · Cited by 95 · see citing works

An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

Dissanayake, T., Rajapaksha, Y., Ragel, R., & Nawinne, I. (2019) · Sensors · doi:10.3390/s19204495 · Cited by 93 · see citing works

From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities

Güneş, H., Piccardi, M., & Pantić, M. (2008) · doi:10.5772/6180 · Cited by 93 · see citing works

Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

van den Broek, E. L., Lisý, V., Janssen, J. H., Westerink, J. H. D. M., Schut, M. H., & Tuinenbreijer, K. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-11721-3_2 · Cited by 92 · see citing works

A real-time EEG-based BCI system for attention recognition in ubiquitous environment

Li, Y., Li, X., Ratcliffe, M., Liu, L., Qi, Y., & Liu, Q. (2011) · doi:10.1145/2030092.2030099 · Cited by 90 · see citing works

Human emotion classification using wavelet transform and KNN

Murugappan, M. (2011) · International Conference on Pattern Analysis and Intelligence Robotics (ICPAIR) · doi:10.1109/icpair.2011.5976886 · Cited by 90 · see citing works

Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals

Khezri, M., Firoozabadi, M., & Sharafat, A. R. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.07.006 · Cited by 89 · see citing works

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Raheel, A., Majid, M., Alnowami, M. R., & Anwar, S. M. (2020) · Sensors · doi:10.3390/s20144037 · Cited by 88 · see citing works

Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques

Balan, O. C., Moise, G., Moldoveanu, A. D. B., Leordeanu, M., & Moldoveanu, F. (2019) · Sensors · doi:10.3390/s19071738 · Cited by 88 · see citing works

Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements

Shan, X. J., & Yang, E. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110305 · Cited by 87 · see citing works

Development of person-independent emotion recognition system based on multiple physiological signals

Kim, K. H., Bang, S. W., & Kim, S. R. (2003) · doi:10.1109/iembs.2002.1134382 · Cited by 16 · see citing works

Physiological signal-based emotion recognition system

Hassani, S., Bafadel, I., Bekhatro, A., Blooshi, E. A., Ahmed, S. G., & Ahmad, M. A. (2017) · IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) · doi:10.1109/icetas.2017.8277912 · Cited by 21 · see citing works

Emotion recognition system design using multi-physiological signals

Jiang, H., Yang, G., Gui, X., Wu, N., & Zhang, T. (2012) · IEEE International Conference on Cognitive Informatics · doi:10.1109/icci-cc.2012.6311199 · Cited by 3 · see citing works

Emotion Recognition Using Physiological Signals

Li, L., & Chen, J. (2006) · Lecture notes in computer science · doi:10.1007/11941354_44 · Cited by 112 · see citing works

An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors

He, C., Yao, Y., & Ye, X. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-2404-7_2 · Cited by 45 · see citing works