Advancing Emotion Classification with Unsupervised Cluster Features for EEG Signals
Keywords:
EEG signals, Soft Labels, Weighting Factors, SVM RBF, Laplacian Eigenmaps, Emotion RecognitionAbstract
Emotion classification based on Electroencephalography (EEG) and physiological signals has gained significant attention in recent years due to its potential applications in affective computing and human-computer interaction. In this paper, we propose a novel algorithm that combines a hybrid feature extraction technique with soft labels and weighting factors to improve emotion classification. Our approach incorporates a hybrid technique that combines Fourier Transform and Time Domain features extracted from EEG recordings with existing features of arousal, valence, and dominance from the dataset. To address overfitting, we employ Laplacian Eigenmaps for dimensionality reduction and unsupervised spectral clustering to derive soft labels. These soft labels enhance the generalizability of the classifier. The classification stage employs a Support Vector Machine with a Radial Basis Function kernel, taking into account the soft labels and a weighting factor based on wheel strength. Experimental results demonstrate the effectiveness of our approach, with improved accuracy and specificity compared to a baseline SVM RBF classifier without soft labels. Therefore, our proposed algorithm offers a promising solution for emotion classification, providing insights into the underlying emotional states captured by EEG and physiological signals.
Downloads
References
Q. Gao, C.H. Wang, Z. Wang, X.L. Song, Y. Song EEG based emotion recognition using fusion feature extraction method Multimed. Tools Appl., 79 (2020), pp. 27057-27074
H. Chao, L. Dong, Y. Liu, B. Lu Improved deep feature learning by synchronization measurements for multi-channel EEG emotion recognition Complexity, 2020 (6816502) (2020)
L.Liu Z.Yin, J.N. Chen, B.X. Zhao, Y.X. Wang Locally robust EEG feature selection for individual-independent emotion recognition Expert Syst. Appl., 162 (113768) (2020)
H. Cui, A.P. Liu, X. Zhang, X. Chen, K.Q. Wang, X. Chen EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network
Y.Q. Yin, X.W. Zheng, B. Hu, Y. Zhang, X.C. Cui EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM Appl. Soft Comput., 100 (106954) (2021)
J. Cheng, M. Chen, C. Li, Y. Liu, X. Chen Emotion recognition from multi-channel EEG via deep forest IEEE J. Biomed. Health (2020), p. 1
H. Yang, J. Han, K. Min A multi-column CNN model for emotion recognition from EEG signals Sensors, 19 (21) (2019)
Y. Luo, B.-.L. Lu EEG data augmentation for emotion recognition using a conditional wasserstein GAN 40th Ann. Int. Conf. IEEE Eng. Med. Bio. Soc (2018), pp. 2535-2538
B.H. Kim, S. JoDeep physiological affect network for the recognition of human emotions IEEE Trans. Affect. Comput., 11 (2) (2020), pp. 230-243
Chatterjee S, Byun YC. EEG-Based Emotion Classification Using Stacking Ensemble Approach. Sensors (Basel). 2022 Nov 6;22(21):8550. doi: 10.3390/s22218550. PMID: 36366249; PMCID: PMC9654664.
Chakravarthi B., Ng S.C., Ezilarasan M., Leung M.F. EEG-based Emotion Recognition Using Hybrid CNN and LSTM Classification. Front. Comput. Neurosci. 2022
Yansari R.T., Mirzarezaee M., Sadeghi M., Araabi B.N. A new survival analysis model in adjuvant Tamoxifen-treated breast cancer patients using manifold-based semi-supervised learning. J. Comput. Sci. 2022;61:101645. doi: 10.1016/j.jocs.2022.10164.
Zhang X, et al. Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine. IEEE Trans Cybernetics. 2021; 51(9): 4386-99. doi: 10.1109/TCYB.2020.2987575.
A mutual information based adaptive windowing of informative EEG for emotion recognition IEEE Trans. Affect. Comput., 11 (4) (2020), pp. 722-735
Liu, Y. & Fu, G. Emotion recognition by deeply learned multi-channel textual and EEG features. Future Gener. Comput. Syst. 119, 1–6.
Rahman, M. A., Hossain, M. F., Hossain, M. & Ahmmed, R. Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal. Egypt. Inform. J. 21, 23–35 (2020).
Nawaz, R., Cheah, K. H., Nisar, H. & Yap, V. V. Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybern. Biomed. Eng. 40(3), 910–926.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
