Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Emotion recognition with deep learning using GAMEEMO data set

Emotion recognition is actively used in brain–computer interface, health care, security, e-commerce, education and entertainment applications to increase and control human–machine interaction. Therefore, emotions affect people's lives and decision-making mechanisms throughout their lives. However, the fact that emotions vary from person to person, being an abstract concept and being dependent on internal and external factors makes the studies in this field difficult. In recent years, studies based on electroencephalography (EEG) signals, which perform emotion analysis in a more robust and reliable way, have gained momentum. In this article, emotion analysis based on EEG signals was performed to predict positive and negative emotions. The study consists of four parts. In the first part, EEG signals were obtained from the GAMEEMO data set. In the second stage, the spectral entropy values of the EEG signals of all channels were calculated and these values were classified by the bidirectional long-short term memory architecture in the third stage. In the last stage, the performance of the deep-learning architecture was evaluated with accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curve. With the proposed method, an accuracy of 76.91% and a ROC value of 90% were obtained.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • 15. Vanluchene, A.L.G., Vereecke, H., Thas, O., et al: ‘Spectral entropy as an electroencephalographic measure of anesthetic drug effect: a comparison with bispectral index and processed midlatency auditory evoked response’, Anesthesiology, 2004, 101, (1), pp. 3442.
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • 10. Russel, A.: ‘Core affect and psychological construction of emotion’, Psychol. Rev., 2003, 110, (1), pp. 145150.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
      • 5. Turnip, A., Simbolon, A.I., Amri, M.F., et al: ‘Backpropagation neural networks training for EEG-SSVEP classification of emotion recognition’, Internetwork. Indonesia J., 2017, 9, (1), pp. 5357.
    15. 15)
      • 2. Alakus, T.B., Turkoglu, I.: ‘EEG based emotion analysis systems’, TBV J. Comput. Sci. Eng., 2018, 11, (1), pp. 2639.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 6. Alakus, T.B., Turkoglu, I.: ‘Determination of effective EEG channels for discrimination of positive and negative emotions with wavelet decomposition and support vector machines’, Int. J. Inform. Technol., 2019, 12, (3), pp. 229237.
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2020.2460
Loading

Related content

content/journals/10.1049/el.2020.2460
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address