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A Comparative Analysis of Machine and Deep Learning Techniques for EEG Evoked Emotion Classification

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Abstract

In the field of Brain Computer Interface (BCI), applications in real life like emotion recognition from recorded electrical activity from brain have become famous topic of research nowadays. Learning successful representations of consistent performances from electroencephalogram (EEG) signals is one of the difficulties in recognition tasks. This research is intended to propose a discriminative and efficacious classification approach for categorizing brain signals patterns depending on the level of activity or frequency for recognizing emotion states. The paper classifies three possible emotion states such as neutral, negative and positive emotional states by operating the Muse EEG headset with four electrode channels (AF7, AF8, TP9, TP10) captured while a subject was watching an emotional video clip on screen. In this experiment various statistical, linear and non linear features are extracted and then Machine and Deep learning based models are implemented to classify the EEG evoked emotions. In this work, a brief comparison study is carried out between the various implemented models with respect to train and test accuracy, recall, precision and F1 score. The highest average accuracy achieved are 98.13% for the proposed Convolutional Neural Network (CNN) model among all implemented Deep learning models and 98.12% for Random forest among the various machine learning techniques implemented. The proposed Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with 97.42 and 97.19% and Decision tree and Support Vector Machine with 96.25 and 96.42% have also provided comparable results for emotion classification respectively.

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References

  1. Cheng, J., Chen, M., Li, C., Liu, Y., Song, R., Liu, A., & Chen, X. (2020). Emotion recognition from multi-channel EEG via deep forest. IEEE Journal of Biomedical and Health Informatics, 25(2), 453–464.

    Article  Google Scholar 

  2. Liu, Yu., Ding, Y., Li, C., Cheng, J., Song, R., Wan, F., & Chen, X. (2020). Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network. Computers in Biology and Medicine, 123, 103927.

    Article  Google Scholar 

  3. Ali, Mouhannad., Mosa, Ahmad Haj., Machot, Fadi Al., & Kyamakya, Kyandoghere. (2016) EEG-based emotion recognition approach for e-healthcare applications. In 2016 eighth international conference on ubiquitous and future networks (ICUFN), pages 946–950. IEEE,

  4. Tao, W., Li, C., Song, R., Cheng, J., Liu, Y., Wan, F., & Chen, X. (2020) EEG-based emotion recognition via channel-wise attention and self attention. IEEE Transactions on Affective Computing, 1–12. https://doi.org/10.1109/TAFFC.2020.3025777

  5. Jenke, R., Peer, A., & Buss, M. (2014). Feature extraction and selection for emotion recognition from EEG. IEESE Transactions on Affective computing, 5(3), 327–339.

    Article  Google Scholar 

  6. Alhagry, S., Fahmy, A. A., & El-Khoribi, R. A. (2017). Emotion recognition based on EEG using LSTM recurrent neural network. Emotion, 8(10), 355–358.

    Google Scholar 

  7. Menezes, M. L. R., Samara, A., Galway, L., Sant’Anna, A., Verikas, A., Alonso-Fernandez, F., et al. (2017). Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset. Personal and Ubiquitous Computing, 21(6), 1003–1013.

    Article  Google Scholar 

  8. De Nadai, Silvia., D’Incà, Massimo., Parodi, Francesco., Benza, Mauro., Trotta, Anita., Zero, Enrico., Zero, Luca., & Sacile, Roberto. (2016) Enhancing safety of transport by road by on-line monitoring of driver emotions. In 2016 11th System of Systems Engineering Conference (SoSE), pages 1–4. Ieee,

  9. Wang, F., Zhong, S. H., Peng, J., Jiang, J., & Liu, Y. (2018) Data augmentation for EEG-based emotion recognition with deep convolutional neural networks. In: International Conference on Multimedia Modeling, (pp. 82–93). Springer

  10. Guo, R., Li, S., He, L., Gao, W., Qi, H., & Owens, G.(2013) Pervasive and unobtrusive emotion sensing for human mental health. In 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, (pp. 436–439). IEEE,

  11. Verschuere, B., Crombez, G., Koster, E., & Uzieblo, K. (2006). Psychopathy and physiological detection of concealed information: A review. Psychologica Belgica, 46, 1–2.

    Article  Google Scholar 

  12. Acharya, U. R., Sree, S. V., Ang, P. C. A., Yanti, R., & Suri, J. S. (2012). Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. International Journal of Neural Systems, 22(02), 1250002.

    Article  Google Scholar 

  13. Kumari, N., Anwar, S., & Bhattacharjee, V. (2020). Correlation and relief attribute rank-based feature selection methods for detection of alcoholic disorder using electroencephalogram signals. IETE Journal of Research, 1–13. https://doi.org/10.1080/03772063.2020.1780166

  14. Anuragi, A., & Sisodia, D. S. (2019). Alcohol use disorder detection using eeg signal features and flexible analytical wavelet transform. Biomedical Signal Processing and Control, 52, 384–393.

    Article  Google Scholar 

  15. Musselman, M., & Djurdjanovic, D. (2012). Time-frequency distributions in the classification of epilepsy from eeg signals. Expert Systems with Applications, 39(13), 11413–11422.

    Article  Google Scholar 

  16. Liu, Y., & Sourina, O. (2013). Real-time fractal-based valence level recognition from EEG. Transactions on computational science XVIII (pp. 101–120). Berlin: Springer.

    Chapter  Google Scholar 

  17. Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology, 29(3), 306–310.

    Article  Google Scholar 

  18. Petrantonakis, P. C., & Hadjileontiadis, L. J. (2009). Emotion recognition from EEG using higher order crossings. IEEE Transactions on Information Technology in Biomedicine, 14(2), 186–197.

    Article  Google Scholar 

  19. Shi, Li-Chen., Jiao, Ying-Ying., & Lu, Bao-Liang. (2013) Differential entropy feature for EEG-based vigilance estimation. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 6627–6630. IEEE,

  20. Lin, Y.-P., Wang, C.-H., Jung, T.-P., Tien-Lin, W., Jeng, S.-K., Duann, J.-R., & Chen, J.-H. (2010). EEG-based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering, 57(7), 1798–1806.

    Article  Google Scholar 

  21. Wang, Z.-M., Shu-Yuan, H., & Song, H. (2019). Channel selection method for EEG emotion recognition using normalized mutual information. IEEE Access, 7, 143303–143311.

    Article  Google Scholar 

  22. Li, Y., Huang, J., Zhou, H., & Zhong, N. (2017). Human emotion recognition with electroencephalographic multidimensional features by hybrid deep neural networks. Applied Sciences, 7(10), 1060.

    Article  Google Scholar 

  23. Kwon Woo Ha and Ji Woo Jeong. (2019). Motor imagery EEG classification using Capsule Networks. Sensors, 19(13), 2854.

    Article  Google Scholar 

  24. Alarcao, S. M., & Fonseca, M. J. (2017). Emotions recognition using EEG signals: A survey. IEEE Transactions on Affective Computing, 10(3), 374–393.

    Article  Google Scholar 

  25. Ackermann, Pascal., Kohlschein, Christian., Bitsch, Jó Agila., Wehrle, Klaus., & Jeschke, Sabina. (2016) Eeg-based automatic emotion recognition: Feature extraction, selection and classification methods. In 2016 IEEE 18th international conference on e-health networking, applications and services (Healthcom), (pp. 1–6). IEEE

  26. Estepp, J. R., & Christensen, J. C. (2015). Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload. Frontiers in Neuroscience, 9, 54.

    Article  Google Scholar 

  27. Zheng, W.-L., & Bao-Liang, L. (2015). Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, 7(3), 162–175.

    Article  Google Scholar 

  28. Naji, M., Firoozabadi, M., & Azadfallah, P. (2015). Emotion classification during music listening from forehead biosignals. Signal, Image and Video Processing, 9(6), 1365–1375.

    Article  Google Scholar 

  29. Nie, Dan., Wang, Xiao-Wei., Shi, Li-Chen., & Lu, Bao-Liang. (2011) EEG-based emotion recognition during watching movies. In 2011 5th International IEEE/EMBS Conference on Neural Engineering, (pp. 667–670). IEEE

  30. Bahari, Fatemeh., & Janghorbani, Amin. (2013) EEG-based emotion recognition using recurrence plot analysis and k nearest neighbor classifier. In 2013 20th Iranian Conference on Biomedical Engineering (ICBME), (pp. 228–233). IEEE

  31. Veeramallu, Gnana Keerthi Priya., Anupalli, Yamuna., Jilumudi, Sravan kumar., & Bhattacharyya, Abhijit. (2019) EEG based automatic emotion recognition using emd and random forest classifier. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), (pp. 1–6). IEEE

  32. Lahane, P., & Sangaiah, A. K. (2015). An approach to EEG based emotion recognition and classification using kernel density estimation. Procedia Computer Science, 48, 574–581.

    Article  Google Scholar 

  33. Subasi, A., Tuncer, T., Dogan, S., Tanko, D., & Sakoglu, U. (2021). EEG-based emotion recognition using tunable q wavelet transform and rotation forest ensemble classifier. Biomedical Signal Processing and Control, 68, 102648.

    Article  Google Scholar 

  34. Martínez-Tejada, Laura Alejandra., Yoshimura, Natsue., & Koike, Yasuharu.(2020) Classifier comparison using EEG features for emotion recognition process. In 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), (pp. 225–230). IEEE,

  35. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

    Article  Google Scholar 

  36. He, Kaiming., Zhang, Xiangyu., Ren, Shaoqing., & Sun, Jian. (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 770–778)

  37. Liu, Xiaodong., He, Pengcheng., Chen, Weizhu., & Gao, Jianfeng. (2019) Multi-task deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504, 2019.

  38. Yao, Z., Wang, Z., Liu, W., Liu, Y., & Pan, J. (2020). Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN. Speech Communication, 120, 11–19.

    Article  Google Scholar 

  39. Kavasidis, Isaak., Palazzo, Simone., Spampinato, Concetto., Giordano, Daniela., & Shah, Mubarak. (2017) Brain2image: Converting brain signals into images. In Proceedings of the 25th ACM international conference on Multimedia, (pp. 1809–1817)

  40. Yang, Yilong., Wu, Qingfeng., Fu, Yazhen., & Chen, Xiaowei. (2018) Continuous convolutional neural network with 3d input for EEG-based emotion recognition. In International Conference on Neural Information Processing, (pp. 433–443). Springer

  41. Zheng, Wei-Long., Zhu, Jia-Yi., Peng, Yong., & Lu, Bao-Liang. (2014) EEG-based emotion classification using deep belief networks. In 2014 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE, 2014

  42. Sun, Bo., Wei, Qinglan., Li, Liandong., Xu, Qihua., He, Jun., & Yu, Lejun. (2016) LSTM for dynamic emotion and group emotion recognition in the wild. In Proceedings of the 18th ACM International Conference on Multimodal Interaction, (pp. 451–457)

  43. Kumari, N., Anwar, S., & Bhattacharjee, V. (2022). Automated visual stimuli evoked multi-channel EEG signal classification using EEGCapsNet. Pattern Recognition Letters, 153, 29–35.

    Article  Google Scholar 

  44. Kumari, N., Anwar, S., & Bhattacharjee, V. (2022). A deep learning-based approach for accurate diagnosis of alcohol usage severity using eeg signals. IETE Journal of Research, 1–15. https://doi.org/10.1080/03772063.2022.2038705

  45. Kumari, N., Anwar, S., & Bhattacharjee, V. (2022) Time series-dependent feature of eeg signals for improved visually evoked emotion classification using EmotionCapsNet. Neural Computing and Applications, 34, 13291–13303.

    Article  Google Scholar 

  46. Bird, Jordan J., Faria, Diego R., Manso, Luis J., Ekárt, Anikó, & Buckingham, ChristopherD. (2019). A deep evolutionary approach to bioinspired classifier optimisation for brain-machine interaction. Complexity, 1–14. https://doi.org/10.1155/2019/4316548

  47. Bird, Jordan J., Manso, Luis J., Ribeiro, Eduardo P., Ekart, Aniko., & Faria, Diego R. (2018) A study on mental state classification using EEG-based brain-machine interface. In 2018 International Conference on Intelligent Systems (IS), (pp. 795–800). IEEE

  48. Li, Zhenqi., Tian, Xiang., Shu, Lin., Xu, Xiangmin., & Hu, Bin.(2017) Emotion recognition from eeg using rasm and lstm. In International Conference on Internet Multimedia Computing and Service, (pp. 310–318). Springer

  49. Cui, H., Aiping Liu, X., Zhang, X. C., Wang, K., & Chen, X. (2020). EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network. Knowledge-Based Systems, 205, 106243.

    Article  Google Scholar 

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  1. Birla Institute of Technology Mesra, Ranchi, 835215, India

    Nandini Kumari, Shamama Anwar & Vandana Bhattacharjee

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  1. Nandini Kumari
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  2. Shamama Anwar
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Kumari, N., Anwar, S. & Bhattacharjee, V. A Comparative Analysis of Machine and Deep Learning Techniques for EEG Evoked Emotion Classification. Wireless Pers Commun 128, 2869–2890 (2023). https://doi.org/10.1007/s11277-022-10076-7

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