Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors
Open accessAbstract
Recent advancements in human-computer interaction research have led to the possibility of emotional communication via brain-computer interface systems for patients with neuropsychiatric disorders or disabilities. In this paper, we efficiently recognize emotional states by analyzing the features of electroencephalography (EEG) signals, which are generated from EEG sensors that noninvasively measure the electrical activity of neurons inside the human brain, and select the optimal combination of these features for recognition. In this paper, the scalp EEG data of 21 healthy subjects (12-14 years old) were recorded using a 14-channel EEG machine while the subjects watched images with four types of emotional stimuli (happy, calm, sad, or scared). After preprocessing, the Hjorth parameters (activity, mobility, and complexity) were used to measure the signal activity of the time series data. We selected the optimal EEG features using a balanced one-way ANOVA after calculating the Hjorth parameters for different frequency ranges. Features selected by this statistical method outperformed univariate and multivariate features. The optimal features were further processed for emotion classification using support vector machine, k-nearest neighbor, linear discriminant analysis, Naive Bayes, random forest, deep learning, and four ensembles methods (bagging, boosting, stacking, and voting). The results show that the proposed method substantially improves the emotion recognition rate with respect to the commonly used spectral power band method.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
What are emotions? And how can they be measured?
Scherer, K. R. (2005) · Social Science Information · doi:10.1177/0539018405058216 · Cited by 4,264 · see citing works
International Affective Picture System (IAPS) : Technical Manual and Affective Ratings
Lang, P. J. (1995) · Medical Entomology and Zoology · Cited by 2,870 · see citing works
Mcgill, R., Tukey, J. W., & Larsen, W. A. (1978) · The American Statistician · doi:10.1080/00031305.1978.10479236 · Cited by 2,250 · see citing works
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1999) · Cited by 1,664 · see citing works
EEG analysis based on time domain properties
Hjorth, B. (1970) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(70)90143-4 · Cited by 1,504 · see citing works
(2002) · Cited by 1,481 · see citing works
Feature Extraction and Selection for Emotion Recognition from EEG
Jenke, R., Peer, A., & BUSS, M. J. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2339834 · Cited by 1,087 · see citing works
Cloud-assisted Industrial Internet of Things (IIoT) – Enabled framework for health monitoring
Hossain, M. S., & Muhammad, G. (2016) · Computer Networks · doi:10.1016/j.comnet.2016.01.009 · Cited by 772 · see citing works
Multimodal Emotion Recognition in Response to Videos
Soleymani, M., Pantić, M., & Pun, T. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.37 · Cited by 725 · see citing works
A systems approach to appraisal mechanisms in emotion
Sander, D., Grandjean, D., & Scherer, K. R. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.03.001 · Cited by 703 · see citing works
McGill, R., Tukey, J. W., & Larsen, W. A. (1978) · The American Statistician · doi:10.2307/2683468 · Cited by 666 · see citing works
Affective video content representation and modeling
Hanjalić, A., & Xu, L.-Q. (2005) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2004.840618 · Cited by 593 · see citing works
Fahoum, A. S. A., & Alfraihat, A. (2014) · ISRN Neuroscience · doi:10.1155/2014/730218 · Cited by 544 · see citing works
Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers
Atkinson, J., & Campos, D. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.049 · Cited by 511 · see citing works
Recognition of emotions using multimodal physiological signals and an ensemble deep learning model
Yin, Z., Zhao, M., Wang, Y., Yang, J., & Zhang, J. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.005 · Cited by 416 · see citing works
EEG-based emotion recognition during watching movies
Nie, D., Wang, X., Shi, L.-C., & Lu, B. (2011) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2011.5910636 · Cited by 375 · see citing works
Time Domain Parameters as a feature for EEG-based Brain–Computer Interfaces
Vidaurre, C., Krämer, N., Blankertz, B., & Schlögl, A. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.07.020 · Cited by 304 · 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
Cloud-Supported Cyber–Physical Localization Framework for Patients Monitoring
Hossain, M. S. (2015) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2470644 · Cited by 273 · see citing works
A Novel EEG Feature Extraction Method Using Hjorth Parameter
Oh, S.-H., Lee, Y., & Kim, H.-N. (2014) · International Journal of Electronics and Electrical Engineering · doi:10.12720/ijeee.2.2.106-110 · Cited by 264 · see citing works
Automatic Removal of Ocular Artifacts in the EEG without an EOG Reference Channel
Gómez-Herrero, G., Clercq, W. D., Anwar, H., Kara, O., Egiazarian, K., Huffel, S. V., & Paesschen, W. V. (2006) · Nordic Signal Processing Symposium · doi:10.1109/norsig.2006.275210 · Cited by 247 · see citing works
Aftanas, L. I., Алексеевич, В. А., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2001) · Neuroscience Letters · doi:10.1016/s0304-3940(01)01703-7 · Cited by 245 · see citing works
Filter Effects and Filter Artifacts in the Analysis of Electrophysiological Data
Widmann, A., & Schröger, E. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00233 · Cited by 241 · see citing works
Fusion of facial expressions and EEG for implicit affective tagging
Koelstra, S., & Patras, I. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.10.002 · Cited by 228 · see citing works
Aftanas, L. I., Алексеевич, В. А., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2002) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(01)00194-5 · Cited by 215 · see citing works
The physical significance of time domain descriptors in EEG analysis
Hjorth, B. (1973) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(73)90260-5 · Cited by 210 · see citing works
Ogino, Y., Nemoto, H., Inui, K., Saito, S., Kakigi, R., & Goto, F. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhl023 · Cited by 187 · see citing works
Bekkedal, M. Y. V., Rossi, J., & Panksepp, J. (2011) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.05.001 · Cited by 157 · see citing works
Software defined healthcare networks
Hu, L., Meikang Qiu, A., Song, J., Hossain, M. S., & Ghoneim, A. (2015) · IEEE Wireless Communications · doi:10.1109/mwc.2015.7368826 · Cited by 125 · see citing works
Patient State Recognition System for Healthcare Using Speech and Facial Expressions
Hossain, M. S. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0627-x · Cited by 106 · see citing works
Mehmood, R. M., & Lee, H. J. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.04.009 · Cited by 99 · see citing works
Self-citationAn Emotion Recognition System for Mobile Applications
Hossain, M. S., & Muhammad, G. (2017) · IEEE Access · doi:10.1109/access.2017.2672829 · Cited by 95 · see citing works
Remarks on emotion recognition from multi-modal bio-potential signals
Takahashi, K. (2005) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2004.1490720 · Cited by 91 · see citing works
Aftanas, L. I., Алексеевич, В. А., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2001) · International Journal of Neuroscience · doi:10.3109/00207450108986547 · Cited by 87 · see citing works
Face-to-Face Communication over the Internet
(2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511977589 · Cited by 71 · see citing works
EEG based Emotion Recognition from Human Brain using Hjorth Parameters and SVM
Mehmood, R. M., & Lee, H. J. (2015) · International Journal of Bio-Science and Bio-Technology · doi:10.14257/ijbsbt.2015.7.3.03 · Cited by 67 · see citing works
Self-citationEmotion-based music recommendation by affinity discovery from film music
Shan, M.-K., Kuo, F.-F., Chiang, M.-F., & Lee, S.-Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.042 · Cited by 66 · see citing works
Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics
Hu, Y., Duan, K., Zhang⋆, Y., Hossain, M. S., Rahman, S. M. M., & Alelaiwi, A. A. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3719-1 · Cited by 60 · see citing works
A review of real-time EEG sonification research
Väljamäe, A., Steffert, T., Holland, S., Marimon, X., Benítez, R., Cincuegrani, S. M., Oliveira, A., & Jordà, S. (2013) · Open Research Online (The Open University) · Cited by 40 · see citing works
I Feel Blue: Robots and Humans Sharing Color Representation for Emotional Cognitive Interaction
Infantino, I., Pilato, G., Rizzo, R., & Vella, F. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-34274-5_30 · Cited by 20 · see citing works
Alpha Activity during Emotional Experience Revealed by ERSP
Du, R., Mehmood, R. M., & Lee, H. (2014) · 網際網路技術學刊 · doi:10.6138/jit.2014.15.5.07 · Cited by 19 · see citing works
Self-citationTowards emotion recognition of EEG brain signals using Hjorth parameters and SVM
Mehmood, R. M., & Lee, H. J. (2015) · Advanced science and technology letters · doi:10.14257/astl.2015.91.05 · Cited by 15 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532 · see citing works
EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508 · see citing works
EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations
Li, P., Liu, H., Si, Y., Li, C., Li, F., Zhu, X., Huang, X., Zeng, Y., Yao, D., Zhang, Y., et al. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2897651 · Cited by 395 · see citing works
Tandel, G. S., Balestrieri, A., Jujaray, T., Khanna, N. N., Saba, L., & Suri, J. S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103804 · Cited by 257 · see citing works
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
Recognition of human emotions using EEG signals: A review
Rahman, M. M., Sarkar, A. K., Hossain, M. A., Hossain, M. S., Islam, M. R., Hossain, M. B., Quinn, J. M. W., & Moni, M. A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104696 · Cited by 226 · see citing works
A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing
Zhang, H., Jolfaei, A., & Alazab, M. (2019) · IEEE Access · doi:10.1109/access.2019.2949741 · Cited by 205 · see citing works
Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques
Islam, M. R., Moni, M. A., Islam, M. M., Rashed-Al-Mahfuz, M., Islam, M. S., Hasan, M. K., Hossain, M. S., Ahmad, M., Uddin, S., Azad, A., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3091487 · Cited by 190 · see citing works
A systematic survey on multimodal emotion recognition using learning algorithms
Ahmed, N., Aghbari, Z. A., & Girija, S. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200171 · Cited by 178 · see citing works
Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning
Bi, X., & Wang, H. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.02.005 · Cited by 169 · see citing works
Automated ASD detection using hybrid deep lightweight features extracted from EEG signals
Bayğın, M., Doğan, Ş., Tuncer, T., Barua, P. D., Faust, O., Arunkumar, N., Abdulhay, E., Palmer, E. E., & Acharya, U. R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104548 · Cited by 160 · see citing works
Samal, P., & Hashmi, M. F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10690-2 · Cited by 163 · see citing works
Machine-Learning-Based Emotion Recognition System Using EEG Signals
Alhalaseh, R., & Alasasfeh, S. (2020) · Computers · doi:10.3390/computers9040095 · Cited by 128 · see citing works
A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application
Orban, M., Elsamanty, M. M., Guo, K., Zhang, S., & Yang, H. (2022) · Bioengineering · doi:10.3390/bioengineering9120768 · Cited by 133 · see citing works
A new fractal pattern feature generation function based emotion recognition method using EEG
Tuncer, T., Doğan, Ş., & Subaşı, A. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110671 · Cited by 126 · see citing works
Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble
Ullah, H., Uzair, M., Mahmood, A., Ullah, M., Khan, S. D., & Cheikh, F. A. (2019) · IEEE Access · doi:10.1109/access.2019.2904400 · Cited by 118 · see citing works
Deep learning based underwater acoustic OFDM communications
Zhang, Y., Li, J., Zakharov, Y. V., Li, X., & Li, J. (2019) · Applied Acoustics · doi:10.1016/j.apacoust.2019.04.023 · Cited by 99 · see citing works
Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform
Taran, S., & Bajaj, V. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3531-0 · Cited by 93 · see citing works
Adaptive Tunable Q Wavelet Transform-Based Emotion Identification
Khare, S. K., Bajaj, V., & Sinha, G. R. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3006611 · Cited by 92 · see citing works
Feature extraction method for classification of alertness and drowsiness states EEG signals
Bajaj, V., Taran, S., Khare, S. K., & Şengür, A. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107224 · Cited by 86 · see citing works
Noghabaei, M., Han, K. K., & Albert, A. (2021) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002130 · Cited by 86 · see citing works
Vempati, R., & Sharma, L. D. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101027 · Cited by 88 · see citing works
CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis
Khan, M. S., Salsabil, N., Alam, M. G. R., Dewan, M. A. A., & Uddin, M. Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-18257-x · Cited by 85 · see citing works
Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy
Li, W., Zhang, Z., & Song, A. (2020) · Measurement · doi:10.1016/j.measurement.2020.108747 · Cited by 79 · see citing works
Automated Feature Extraction on AsMap for Emotion Classification Using EEG
Ahmed, M. Z. I., Sinha, N., Phadikar, S., & Ghaderpour, E. (2022) · Sensors · doi:10.3390/s22062346 · Cited by 75 · see citing works
Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals
Lu, Y., Wang, M., Wu, W., Han, Y., Zhang, Q., & Chen, S. (2020) · Measurement · doi:10.1016/j.measurement.2019.107003 · Cited by 73 · see citing works
Yuvaraj, R., Thangavel, P., Thomas, J., Fogarty, J. S., & Ali, F. (2023) · Sensors · doi:10.3390/s23020915 · Cited by 73 · see citing works
Self-Paced Dynamic Infinite Mixture Model for Fatigue Evaluation of Pilots’ Brains
Wu, E. Q., Zhou, M., Hu, D., Zhu, L., Tang, Z., Qiu, X.-Y., Deng, P.-Y., Zhu, L., & He, R. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3033005 · Cited by 72 · see citing works
Densely Feature Fusion Based on Convolutional Neural Networks for Motor Imagery EEG Classification
Li, D., Wang, J., Xu, J., & Fang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2941867 · Cited by 71 · see citing works
EEG-based emotion recognition systems; comprehensive study
Hamzah, H. A., & Abdalla, K. K. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31485 · Cited by 82 · see citing works
An Evolutionary Optimized Variational Mode Decomposition for Emotion Recognition
Khare, S. K., & Bajaj, V. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3020915 · Cited by 70 · see citing works
Li, R., Ren, C., Zhang, X., & Hu, B. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105080 · Cited by 69 · see citing works
Al-Shargie, F., Tariq, U., Alex, M., Mir, H., & Al-Nashash, H. A. (2019) · IEEE Access · doi:10.1109/access.2019.2944008 · Cited by 67 · see citing works
Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition
Chen, H., Sun, S., Li, J., Yu, R., Li, N., Li, X., & Hu, B. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3137857 · Cited by 63 · see citing works
Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition
Fang, Y., Yang, H., Zhang, X., Liu, H., & Tao, B. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.617531 · Cited by 62 · see citing works
A Review of Processing Methods and Classification Algorithm for EEG Signal
Xie, Y., & Oniga, S. (2020) · Carpathian Journal of Electronic and Computer Engineering · doi:10.2478/cjece-2020-0004 · Cited by 60 · see citing works
Emotion classification using EEG signals based on tunable‐ Q wavelet transform
Krishna, A. H., Sri, A. B., Priyanka, K. Y. V. S., Taran, S., & Bajaj, V. (2018) · IET Science Measurement & Technology · doi:10.1049/iet-smt.2018.5237 · Cited by 61 · see citing works
Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel
Zhang, Y., Li, J., Zakharov, Y. V., Li, J., Li, Y., Lin, C., & Li, X. (2019) · IEEE Access · doi:10.1109/access.2019.2906424 · Cited by 59 · see citing works
Emotion Feature Analysis and Recognition Based on Reconstructed EEG Sources
Chen, G., Zhang, X., Sun, Y., & Zhang, J. (2020) · IEEE Access · doi:10.1109/access.2020.2966144 · Cited by 59 · see citing works
Sharma, M. D., Darji, J., Thakrar, M., & Acharya, U. R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105224 · Cited by 59 · see citing works
Comparative analysis of different characteristics of automatic sleep stages
Zhao, D., Wang, Y., Wang, Q., & Wang, X. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.04.004 · Cited by 58 · see citing works
Ahmed, M. Z. I., Sinha, N., Ghaderpour, E., Phadikar, S., & Ghosh, R. (2023) · Bioengineering · doi:10.3390/bioengineering10010054 · Cited by 56 · see citing works
EEG-based emotion analysis using non-linear features and ensemble learning approaches
Rahman, M. M., Sarkar, A. K., Hossain, M. A., & Moni, M. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118025 · Cited by 55 · see citing works
EEG-based affective state recognition from human brain signals by using Hjorth-activity
Mehmood, R. M., Bilal, M., Vimal, S., & Lee, S. (2022) · Measurement · doi:10.1016/j.measurement.2022.111738 · Cited by 50 · see citing works
A survey of 2D shape representation: Methods, evaluations, and future research directions
Kurnianggoro, L., Wahyono, W., & Jo, K.-H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.02.093 · Cited by 51 · see citing works
Houssein, E. H., Hammad, A., Emam, M., & Ali, A. A. (2024) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2024.108329 · Cited by 52 · see citing works
EEG Feature Selection via Global Redundancy Minimization for Emotion Recognition
Xu, X., Jia, T., Li, Q., Wei, F., Ye, L., & Wu, X. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3068496 · Cited by 48 · see citing works
Goshvarpour, A., & Goshvarpour, A. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09699-z · Cited by 48 · see citing works
Rabčan, J., Levashenko, V., Zaitseva, E., & Kvaššay, M. (2020) · IEEE Access · doi:10.1109/access.2020.3031447 · Cited by 48 · see citing works
Recent advances and current trends in brain‐computer interface research and their applications
Žabčíková, M., Koudelková, Z., Jašek, R., & Lorenzo-Navarro, J. (2021) · International Journal of Developmental Neuroscience · doi:10.1002/jdn.10166 · Cited by 47 · see citing works
Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm
Lu, W., Wei, Y., Yuan, J., Deng, Y., & Song, A. (2020) · IEEE Access · doi:10.1109/access.2020.3021051 · Cited by 43 · see citing works
Diagnosis of mild Alzheimer's disease by EEG and ERP signals using linear and nonlinear classifiers
Rad, E. M., Azarnoosh, M., Ghoshuni, M., & Khalilzadeh, M. M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103049 · Cited by 42 · see citing works
Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Model
Ha, C.-B., & Song, H. (2018) · IEEE Access · doi:10.1109/access.2018.2825463 · Cited by 40 · see citing works
Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey
Gonzalez, H. A., George, R., Muzaffar, S., Acevedo, J., Höppner, S., Mayr, C., Yoo, J., Fitzek, F. H. P., & Elfadel, I. M. (2021) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2021.3089132 · Cited by 37 · see citing works
Optimized Tunable Q Wavelet Transform Based Drowsiness Detection from Electroencephalogram Signals
Khare, S. K., & Bajaj, V. (2020) · IRBM · doi:10.1016/j.irbm.2020.07.005 · Cited by 36 · see citing works
Machine Learning Model for Adaptive Modulation of Multi-Stream in MIMO-OFDM System
Ha, C.-B., You, Y., & Song, H. (2018) · IEEE Access · doi:10.1109/access.2018.2889076 · Cited by 35 · see citing works
Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm
Arora, T. K., Chaubey, P. K., Raman, M. S., Kumar, B., Nagesh, Y., Anjani, P. K., Ahmed, H. M. S., Hashmi, A., Balamuralitharan, S., & Debtera, B. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8379202 · Cited by 34 · see citing works
Pei, G., Shang, Q., Hua, S., Li, T., & Jin, J. K. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108085 · Cited by 33 · see citing works
Zhang, S., Zhu, Z., Zhang, B., Feng, B., Yu, T., & Li, Z. (2020) · Sensors · doi:10.3390/s20174749 · Cited by 34 · see citing works
Shen, L., Zhang, H., Zhu, C., Li, R., Qian, K., Tian, F., Hu, B., Schuller, B. W., & Yamamoto, Y. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3557873 · Cited by 31 · see citing works
Aslam, A. R., & Altaf, M. A. B. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702738 · Cited by 31 · see citing works
Review of Closed-Loop Brain–Machine Interface Systems From a Control Perspective
Pan, H., Song, H., Zhang, Q., & Mi, W. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3138677 · Cited by 29 · see citing works
Children Emotion Regulation: Development of Neural Marker by Investigating Human Brain Signals
Mehmood, R. M., Yang, H.-J., & Kim, S. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3011817 · Cited by 28 · see citing works
Emotion Recognition System Based on Two-Level Ensemble of Deep-Convolutional Neural Network Models
Hussain, M., Qazi, E.-U.-H., Aboalsamh, H., & Ullah, I. (2023) · IEEE Access · doi:10.1109/access.2023.3245830 · Cited by 28 · see citing works
Maswanganyi, R. C., Tu, C., Owolawi, P. A., & Du, S. (2022) · IEEE Access · doi:10.1109/access.2022.3205734 · Cited by 26 · see citing works
Asghar, M. A., Khan, M. J., Shahid, H., Shorfuzzaman, M., Xiong, N. N., & Mehmood, R. M. (2021) · IEEE Access · doi:10.1109/access.2021.3051808 · Cited by 26 · see citing works
EEG-Based Emotion Recognition of Deaf Subjects by Integrated Genetic Firefly Algorithm
Tian, Z., Li, D., Song, Y., Gao, Q., Kang, Q., & Yang, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3121473 · Cited by 26 · see citing works
Emotion identification by dynamic entropy and ensemble learning from electroencephalogram signals
Ashokkumar, S. R., Anupallavi, S., Mohanbabu, G., Premkumar, M. J., & Jeevanantham, V. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22670 · Cited by 26 · see citing works
Cross-Correlation Aided Ensemble of Classifiers for BCI Oriented EEG Study
Paranjape, P. N., Dhabu, M. M., Deshpande, P. S., & Kekre, A. M. (2019) · IEEE Access · doi:10.1109/access.2019.2892492 · Cited by 25 · see citing works
Feature Selection for High Dimensional Data Using Monte Carlo Tree Search
Chaudhry, M. U., & Lee, J.-H. (2018) · IEEE Access · doi:10.1109/access.2018.2883537 · Cited by 24 · see citing works
Real-time emotion recognition system with multiple physiological signals
Zhuang, J.-R., Guan, Y.-J., Nagayoshi, H., Muramatsu, K., WATANUKI, K., & Tanaka, E. (2019) · Journal of Advanced Mechanical Design Systems and Manufacturing · doi:10.1299/jamdsm.2019jamdsm0075 · Cited by 23 · see citing works
Nandy, S., Adhikari, M., Chakraborty, S., Alkhayyat, A. H., & Kumar, N. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.12.019 · Cited by 22 · see citing works
Sughasiny, M., & Rajeshwari, J. (2018) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac.2018.8653654 · Cited by 22 · see citing works
RetractedXu, X., Mazloom, R., Goligerdian, A., Staley, J. M., Amini, M., Wyckoff, G. J., Riviere, J. E., & Jaberi‐Douraki, M. (2019) · Topics in companion animal medicine · doi:10.1016/j.tcam.2019.100366 · Cited by 22 · see citing works
Imbalanced data learning using SMOTE and deep learning architecture with optimized features
Alex, S. A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10481-y · Cited by 21 · see citing works
Diagnosis and Intervention for Children With Autism Spectrum Disorder: A Survey
Wang, Z., Liu, J., Zhang, W., Nie, W., & Liu, H. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3093040 · Cited by 20 · see citing works
Echtioui, A., Mlaouah, A., Zouch, W., Ghorbel, M., Mhiri, C., & Hamam, H. (2021) · Applied Sciences · doi:10.3390/app11219948 · Cited by 20 · see citing works
A Customized ECA-CRNN Model for Emotion Recognition Based on EEG Signals
Song, Y. X., Yin, Y., & Xu, P. (2023) · Electronics · doi:10.3390/electronics12132900 · Cited by 19 · see citing works
Spatio-temporal CNN-BiLSTM dynamic approach to emotion recognition based on EEG signal
Redwan, U. G., Zaman, T., & Mizan, H. B. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110277 · Cited by 19 · see citing works
A user-centered approach for detecting emotions with low-cost sensors
Francese, R., Risi, M., & Tortora, G. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09576-0 · Cited by 18 · see citing works
Human Emotion Recognition from EEG-brain Signals using Enhanced Machine Learning Method
Ghous, G., Najam, S., & Jalal, A. (2025) · International Conference on Advancements in Computational Sciences (ICACS) · doi:10.1109/icacs64902.2025.10937822 · Cited by 17 · see citing works
Stehle, S. A., Aubonnet, R., Hassan, M., Recenti, M., Jacob, D., Petersen, H., & Gargiulo, P. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.1038976 · Cited by 17 · see citing works
Yoon, H., Klasky, H. B., Gounley, J. P., Alawad, M., Gao, S. B., Durbin, E. B., Wu, X., Stroup, A. M., Doherty, J. A., Coyle, L. M., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103564 · Cited by 16 · see citing works
Choy, S.-M., Cheng, E., Wilkinson, R. H., Burnett, L., & Austin, M. W. (2021) · IEEE Access · doi:10.1109/access.2021.3049798 · Cited by 16 · see citing works
Mahato, S., Paul, S., Goyal, N., Mohanty, S. N., & Jain, S. (2022) · Recent Patents on Engineering · doi:10.2174/1872212117666220801105612 · Cited by 16 · see citing works
Li, Y., Liu, Q., & Wu, J. X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103671 · Cited by 19 · see citing works
Interactive multi-agent convolutional broad learning system for EEG emotion recognition
Shi, S., Liu, W., & Chen, C. L. P. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125420 · Cited by 18 · see citing works
Miah, M. O., Muhammod, R., Mamun, K. A., Farid, D. M., Kumar, S., Sharma, A. K., & Dehzangi, A. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109373 · Cited by 16 · see citing works
Interpretable Emotion Classification Using Multidomain Feature of EEG Signals
Zhao, K., Xu, D., He, K., & Peng, G. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3266322 · Cited by 15 · see citing works
He, W., Wang, G., Hu, J., Li, C., Guo, B., & Li, F. (2019) · IEEE Access · doi:10.1109/access.2019.2921568 · Cited by 15 · see citing works
Mood classification through physiological parameters
Cavallo, F., Semeraro, F., Mancioppi, G., Betti, S., & Fiorini, L. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01595-6 · Cited by 15 · see citing works
FBCSP-based Multi-class Motor Imagery Classification using BP and TDP features
Abbas, W., & Khan, N. A. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512238 · Cited by 14 · see citing works
Accelerated sparsity based reconstruction of compressively sensed multichannel EEG signals
Tayyib, M., Amir, M., Javed, U., Akram, M. W., Yousufi, M., Qureshi, I. M., Abdullah, S., & Ullah, H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0225397 · Cited by 13 · see citing works
A Comprehensive Study on Human Activity Recognition
Aarthi, S., & Juliet, S. (2021) · doi:10.1109/icspc51351.2021.9451759 · Cited by 13 · see citing works
EEG‐Based Emotion Recognition Datasets for Virtual Environments: A Survey
Hamzah, H. A., & Abdalla, K. K. (2024) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2024/6091523 · Cited by 14 · see citing works
Self-attention-based 1DCNN model for multiclass EEG emotion classification
Raikwar, S., & Mayuri, A. V. R. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07015-1 · Cited by 12 · see citing works
Yu, S., Wang, S., & Chang, Y. P. (2022) · IEEE Access · doi:10.1109/access.2022.3221774 · Cited by 12 · see citing works
Video Based Sub-Categorized Facial Emotion Detection Using LBP and Edge Computing
Kulkarni, P., & Rajesh, T. M. (2021) · Revue d intelligence artificielle · doi:10.18280/ria.350106 · Cited by 11 · see citing works
Reddy, C. N., Mahesh, S., & Manjunathachari, K. (2023) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2023.2252551 · Cited by 11 · see citing works
Deep Learning Methods for EEG Neural Classification
Nakagome, S., Craik, A., Ravindran, A. S., He, Y., Cruz-Garza, J. G., & Contreras-Vidal, J. L. (2022) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_78-1 · Cited by 12 · see citing works
- Authors
- Majid Mehmood, R., Du, R., & Lee, H. J.
- Affiliations
- Nanjing University of Posts and Telecommunications (CN)
- Jeonbuk National University (KR)
- Venue
- IEEE Access
- Published
- 2017
- Volume
- 5
- Pages
- 14797-14806
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2740113900
- Cited by
- 213
- References
- 43
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 20 · 2023: 31 · 2022: 27 · 2021: 40 · 2020: 29 · 2019: 29 · 2018: 14
- FWCI
- 25.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Brain Korea 21 PLUS Project
- National Research Foundation (NRF) of Korea
- Ministry of Science, ICT and Future Planning, Korea, under the Information Technology Research Center, supervised by the Institute for Information and Communications Technology Promotion (IITP-2016-R0992-16-1023)
- Basic Science Research Program through the NRF of South Korea, through the Ministry of Education (GR 2016R1D1A3B03931911)
- National Natural Science Foundation for Young Scholars of China (61603198)
- Natural Science Foundation for Young Scholars of Jiangsu Province (BK20160918)
- NUPTSF (NY214194)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- emotion recognition, EEG, feature selection, Hjorth parameters, brain-computer interface, EEG classification, ensemble learning, emotional stimuli, children
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
