Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition

  • Authors

    • Abdullah Yousef Al-Qammaz
    • Farzana Kabir Ahmad
    • Yuhanis Yusof
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.15.11373
  • Electroencephalogram, Human emotion recognition, Social spider optimization, Swarm intelligence methods.

  • Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51 7.17 and 60.97 8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively.

     

     

  • References

    1. [1] Liu Y & Sourina O (2013), EEG databases for emotion recognition. Proceedings of the IEEE International Conference on Cyberworlds, pp. 302–309.

      [2] Kim MK, Kim M, Oh E & Kim SP (2013), A review on the computational methods for emotional state estimation from the human EEG. Computational and Mathematical Methods in Medicine 2013, 1–13.

      [3] Kumar S & Lopez D (2014), A review on feature selection methods for high dimensional data. International Journal of Engineering and Technology 8, 669–672.

      [4] Al-Qammaz AY, Ahmad FK & Yusof Y (2016), Optimisation of least squares support vector machine technique using genetic algorithm for electroencephalogram multi-dimensional signals. Jurnal Teknologi 78, 107–115.

      [5] Hsu WY & Hu YP (2015), Artificial bee colony algorithm for single-trial electroencephalogram analysis. Clinical EEG and Neuroscience 46, 119–125.

      [6] Yavuz G & Aydin D (2016), Angle modulated artificial bee colony algorithms for feature selection. Applied Computational Intelligence and Soft Computing 2016, 1–7.

      [7] Wang J & Zhao Y (2014), EEG signal classification with feature selection based on one-dimension real valued particle swarm optimization. Proceedings of the International Conference on Mechatronics, Control and Electronic Engineering, pp. 310-314.

      [8] Binitha S & Sathya SS (2012), A survey of bio inspired optimization algorithms. International Journal of Soft Computing and Engineering 2, 137–151.

      [9] Cuevas E, Cienfuegos M, Zaldívar D & Pérez-Cisneros M (2013), A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications 40, 6374–6384.

      [10] Pereira LA, Rodrigues D, Ribeiro PB, Papa JP & Weber SA (2014), Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification. Proceedings of the IEEE 27th International Symposium on Computer-Based Medical Systems, pp. 14–17.

      [11] Wali MK, Murugappan M & Ahmmad B (2013), Wavelet packet transform based driver distraction level classification using EEG. Mathematical Problems in Engineering 2013, 1–10.

      [12] Koelstra S, Muhl C, Soleymani M, Lee JS, Yazdani A, Ebrahimi T, Pun T, Nijholt A & Patras I (2012), Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing 3, 18–31.

      [13] Jirayucharoensak S, Pan-Ngum S & Israsena P (2014), EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Scientific World Journal 2014, 1–10.

      [14] Suykens JA & Vandewalle J (1999), Least squares support vector machine classifiers. Neural Processing Letters 9, 293–300.

      [15] Mustaffa Z & Yusof Y (2014), LSSVM parameters tuning with enhanced artificial bee colony. International Arab Journal of Information Technology 11, 236–242.

      [16] Lai L & Liu J (2014), Support vector machine and least square support vector machine stock forecasting models. Computer Science and Information Technology 2, 30–39.

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  • How to Cite

    Yousef Al-Qammaz, A., Kabir Ahmad, F., & Yusof, Y. (2018). Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition. International Journal of Engineering & Technology, 7(2.15), 146-149. https://doi.org/10.14419/ijet.v7i2.15.11373