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Abstract
It is incredibly challenging to build an intelligent algorithm for emotion recognition that can deliver high accuracy because electroencephalography (EEG) signals are not stationary, nonlinear, and noisy. First, decomposing the preprocessed EEG signals of the SEED dataset into five frequency bands: delta, theta, alpha, beta, and gamma, and then calculated their energy and entropy from the extracted features. Then Principal Component Analysis (PCA) method for feature reduction was performed. It is important to note that different types of wavelets transform (db6, db5, etc.) were tested, and hyperparameter tuning of classification models was done to obtain optimal accuracy. The next step is classifying the emotions into three states: -1(negative), 0(neutral), and +1(positive), and tested the dataset on two types of classification models, namely Random Forest and Support vector machine (SVM). SVM gives better performance compared to Random Forest with an accuracy of 80.74%.
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References
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- T.H. Priya, P. Mahalakshmi, V. Naidu, and M. Srinivas, “Stress Detection from EEG Using Power Ratio,” 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020, pp. 1-6.
- K. Manjula and M.B. Anandaraju, “A Comparative Study on Feature Extraction and Classification of Mind Waves for Brain-Computer Interface (BCI),” International Journal of Engineering & Technology, vol. 7, no. 1.9, 132–136, 2018.
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References
P. Santhiya and S. Chitrakala, “A Survey on Emotion Recognition from EEG Signals: Approaches, Techniques & Challenges,” 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019, pp. 1–6.
G.S. Yadav, F.J. Cidral-Filho, and R.B. Iyer, “Using Heartfulness Meditation and Brainwave Entrainment to Improve Teenage Mental Wellbeing,” Frontiers in Psychology, 2021, 12:742892.
J. Preethi, M. Sreeshakthy, and A. Dhilipan, “A Survey on EEG Based Emotion Analysis using various Feature Extraction Techniques,” International Journal of Science Engineering and Technology Research (IJSETR), vol. 3, no. 11, November 2014.
N. Jatupaiboon, S. Pan-ngum, P. Israsena, “Real-Time EEG-Based Happiness Detection System,” The Scientific World Journal, vol. 2013, pp. 1–12, 2013.
H. Shao, J. Wang, Y. Wang, Y. Yao, and J. Liu, “EEG-Based Emotion Recognition with Deep Convolutional Neural Network,” 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS), 2019, pp. 1225-1229.
T.H. Priya, P. Mahalakshmi, V. Naidu, and M. Srinivas, “Stress Detection from EEG Using Power Ratio,” 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020, pp. 1-6.
K. Manjula and M.B. Anandaraju, “A Comparative Study on Feature Extraction and Classification of Mind Waves for Brain-Computer Interface (BCI),” International Journal of Engineering & Technology, vol. 7, no. 1.9, 132–136, 2018.
A. Topic and M. Russo, “Emotion Recognition Based on EEG Feature Maps through Deep Learning Network,” Engineering Science and Technology and International Journal, vol. 24, no. 6, December 2021.
K. Guo, H. Mei, X. Xie, and X. Xu, “A Convolutional Neural Network Feature Fusion Framework with Ensemble Learning for EEG-based Emotion Classification,” IEEE MTT-S International Microwave Biomedical Conference (IMBioC), 2019, pp. 1–4
H. Chao, L. Dong, Y. Liu, and B. Lu, “Emotion Recognition from Multiband EEG Signals Using CapsNet,” Sensors, vol. 19, no. 9, pp. 2212, 2019.