Emotion classification using EEG signals and machine learning methods
الموضوعات :Mohammad Adeli 1 , Mehrnoosh Rezayati 2
1 - Department of biomedical engineering, Dezful branch, Islamic Azad university, Dezful, Iran.
2 - Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.
الکلمات المفتاحية: EEG, emotion classification, machine learning, feature extraction, wavelet,
ملخص المقالة :
In this paper, a method of emotion classification from EEG signals is presented. This method comprises four steps of pre-processing by low-pass filtering, feature extraction using the discrete wavelet transform and the Wigner–Ville distribution, dimensionality reduction using linear dis-criminant analysis, and classification. The k nearest neighbors, random forests, and support vector machines were used as classification models. The results of this study showed that the features extracted by the wavelet transform and Wigner–Ville distribution led to the improvement of classification accuracy compared to other studies. In addition, the highest accuracy of 94.1% for the classification of 4 emotions was obtained using the features of the TP9 electrode and the support vector machine classifier.
[1]. Wang, J. and M. Wang, Review of the emotional feature extraction and classification using EEG signals. Cognitive robotics, 2021. 1: p. 29-40.
[2]. Suhaimi, N.S., J. Mountstephens, and J. Teo, EEG-based emotion recognition: A state-of-the-art review of current trends and opportunities. Computational intelligence and neuroscience, 2020. 2020.
[3]. Chang, H., Y. Zong, W. Zheng, C. Tang, J. Zhu, and X. Li, Depression assessment method: an eeg emotion recognition framework based on spatiotemporal neural network. Frontiers in Psychiatry, 2022. 12: p. 837149.
[4]. Dev, A., N. Roy, M.K. Islam, C. Biswas, H.U. Ahmed, M.A. Amin, F. Sarker, R. Vaidyanathan, and K.A. Mamun, Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review. IEEE Access, 2022. 10: p. 16756-16781.
[5]. Kaur, B., D. Singh, and P.P. Roy, EEG based emotion classification mechanism in BCI. Procedia computer science, 2018. 132: p. 752-758.
[6]. Al-Nafjan, A., M. Hosny, Y. Al-Ohali, and A. Al-Wabil, Review and classification of emotion recognition based on EEG brain-computer interface system research: a systematic review. Applied Sciences, 2017. 7(12): p. 1239.
[7]. Pei, G. and T. Li, A literature review of EEG-based affective computing in marketing. Frontiers in Psychology, 2021. 12: p. 602843.
[8]. Liu, Y., O. Sourina, and M.R. Hafiyyandi. EEG-based emotion-adaptive advertising. in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. 2013. IEEE.
[9]. Bălan, O., G. Moise, L. Petrescu, A. Moldoveanu, M. Leordeanu, and F. Moldoveanu, Emotion classification based on biophysical signals and machine learning techniques. Symmetry, 2019. 12(1): p. 21.
[10]. Zhang, J., Z. Yin, P. Chen, and S. Nichele, Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion, 2020. 59: p. 103-126.
[11]. Domínguez-Jiménez, J.A., K.C. Campo-Landines, J.C. Martínez-Santos, E.J. Delahoz, and S.H. Contreras-Ortiz, A machine learning model for emotion recognition from physiological signals. Biomedical signal processing and control, 2020. 55: p. 101646.
[12]. Liu, J., H. Meng, M. Li, F. Zhang, R. Qin, and A. Nandi, Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction. Concurrency and Computation: Practice and Experience, 2018. 30: p. e4446.
[13]. Acharya, D., R. Jain, S.S. Panigrahi, R. Sahni, S. Jain, S.P. Deshmukh, and A. Bhardwaj. Multi-class emotion classification using EEG signals. in Advanced Computing: 10th International Conference, IACC 2020, Panaji, Goa, India, December 5–6, 2020, Revised Selected Papers, Part I 10. 2021. Springer.
[14]. Ghosh, S.M., S. Bandyopadhyay, and D. Mitra, Nonlinear classification of emotion from EEG signal based on maximized mutual information. Expert Systems with Applications, 2021. 185: p. 115605.
[15]. Suhaimi, N.S., J. Mountstephens, and J. Teo, A dataset for emotion recognition using virtual reality and EEG (DER-VREEG): emotional state classification using low-cost wearable VR-EEG headsets. Big Data and Cognitive Computing, 2022. 6(1): p. 16.
[16]. Bhardwaj, A., A. Gupta, P. Jain, A. Rani, and J. Yadav. Classification of human emotions from EEG signals using SVM and LDA Classifiers. in 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). 2015.
[17]. Al-Qerem, A., F. Kharbat, S. Nashwan, S. Ashraf, and K. Blaou, General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution. International Journal of Distributed Sensor Networks, 2020. 16(3): p. 1550147720911009.
[18]. Saeidi, M., W. Karwowski, F.V. Farahani, K. Fiok, R. Taiar, P.A. Hancock, and A. Al-Juaid, Neural decoding of EEG signals with machine learning: a systematic review. Brain Sciences, 2021. 11(11): p. 1525.
[19]. Alsalmi, H. and Y. Wang, Mask filtering to the Wigner-Ville distribution. Geophysics, 2021. 86(6): p. V489-V496.
[20]. Xanthopoulos, P., P.M. Pardalos, T.B. Trafalis, P. Xanthopoulos, P.M. Pardalos, and T.B. Trafalis, Linear discriminant analysis. Robust data mining, 2013: p. 27-33.
[21]. Gu, Q., Z. Li, and J. Han. Linear discriminant dimensionality reduction. in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I 11. 2011. Springer.
[22]. Sokolova, M. and G. Lapalme, A systematic analysis of performance measures for classification tasks. Information processing & management, 2009. 45(4): p. 427-437.
[23]. Gannouni, S., A. Aledaily, K. Belwafi, and H. Aboalsamh, Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification. Scientific Reports, 2021. 11(1): p. 7071.
[24]. Şengür, D. and S. Siuly, Efficient approach for EEG-based emotion recognition. Electronics Letters, 2020. 56(25): p. 1361-1364.
[25]. Subasi, A., T. Tuncer, S. Dogan, D. Tanko, and U. Sakoglu, EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier. Biomedical Signal Processing and Control, 2021. 68: p. 102648.
[26]. Houssein, E.H., A. Hammad, and A.A. Ali, Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. Neural Computing and Applications, 2022. 34(15): p. 12527-12557.
[27]. Qing, C., R. Qiao, X. Xu, and Y. Cheng, Interpretable emotion recognition using EEG signals. Ieee Access, 2019. 7: p. 94160-94170.
[28]. Chakladar, D.D. and S. Chakraborty, EEG based emotion classification using “correlation based subset selection”. Biologically inspired cognitive architectures, 2018. 24: p. 98-106.
[29]. Huang, C., Recognition of psychological emotion by EEG features. Network Modeling Analysis in Health Informatics and Bioinformatics, 2021. 10: p. 1-11.
[30]. Pane, E.S., A.D. Wibawa, and M.H. Purnomo, Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters. Cognitive processing, 2019. 20: p. 405-417.
