AN IMPROVEMENT TO EMOTION DETECTION IN EEG SIGNALS USING DEEP ARTIFICIAL NEURALNETWORKS

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MOUSAVINASR SEYED MOHAMMAD REZA; POURMOHAMMAD ALI

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Journal: Medical Journal of Tabriz University of Medical Sciences Year:2019 | Volume:40 | Issue:5 Page(s): 92-101

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Title

AN IMPROVEMENT TO EMOTION DETECTION IN EEG SIGNALS USING DEEP ARTIFICIAL NEURALNETWORKS

Author(s)

MOUSAVINASR SEYED MOHAMMAD REZA | POURMOHAMMAD ALI | Issue Writer Certificate

Keywords

DEEP NEURAL NETWORKSQ1

ELECTROENCEPHALOGRAPHY (EEG)Q2

EMOTION DETECTIONQ1

ICAQ1

Abstract

Background: One of the research areas that in recent years several studies have been performed on it isemotion recognition in the EEG signals. In this study, a 4-layered approach has been provided to improve theemotion detection in EEG signals. Methods: In this study, we used DEAP data set. We provided a 4-layered approach as follows: 1-Preprocessing2-Feature Extraction 3-Dimensionality Reduction 4-Emotion detection. To select optimal choices in some stages ofthese layers, we’ ve done some other experiments. Results: The three different experiments have been done. First, finding the right window in the feature extraction. The results shows that Hamming window was the suitable one. Second, selecting the most appropriate number offilter banks in the feature extraction. The results of this experiment showed that 26 numbers was the most appropriatechoice. The third experiment was to detect emotions through the proposed method. The results showed 81. 58 percentaccuracy for arousal, 79. 87 percent accuracy for the valence, 80. 35 percent accuracy for the dominance dimensionsin 2-classes experiment. For 3-classes experiment the results was 68. 54 percent accuracy for arousal 66. 31 percentaccuracy for the valence, 66. 92 percent accuracy for the dominance dimensions. Conclusion: The 7. 38 percent accuracy improvement in 2-class experiment and 3. 38 accuracy improvement in3-class experiment. This improvement in valence dimension was 7. 54 and 5. 21, respectively. It seems that using theproposed method can improve EMOTION DETECTION in EEG signals.

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APA: Copy

MOUSAVINASR, SEYED MOHAMMAD REZA, & POURMOHAMMAD, ALI. (2019). AN IMPROVEMENT TO EMOTION DETECTION IN EEG SIGNALS USING DEEP ARTIFICIAL NEURALNETWORKS. MEDICAL JOURNAL OF TABRIZ UNIVERSITY OF MEDICAL SCIENCES, 40(5 ), 92-101. SID. https://sid.ir/paper/46527/en

Vancouver: Copy

MOUSAVINASR SEYED MOHAMMAD REZA, POURMOHAMMAD ALI. AN IMPROVEMENT TO EMOTION DETECTION IN EEG SIGNALS USING DEEP ARTIFICIAL NEURALNETWORKS. MEDICAL JOURNAL OF TABRIZ UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2019;40(5 ):92-101. Available from: https://sid.ir/paper/46527/en

IEEE: Copy

SEYED MOHAMMAD REZA MOUSAVINASR, and ALI POURMOHAMMAD, “AN IMPROVEMENT TO EMOTION DETECTION IN EEG SIGNALS USING DEEP ARTIFICIAL NEURALNETWORKS,” MEDICAL JOURNAL OF TABRIZ UNIVERSITY OF MEDICAL SCIENCES, vol. 40, no. 5 , pp. 92–101, 2019, [Online]. Available: https://sid.ir/paper/46527/en

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