Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

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Zangeneh Soroush Morteza; MAGHOOLI KEIVAN; SETAREHDAN SEYED KAMALEDIN; MOTIE NASRABADI ALI

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Journal Paper

Paper Information

Journal: International Clinical Neuroscience Journal Year:2018 | Volume:5 | Issue:4 Page(s): 0-0

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Title

Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

Author(s)

Zangeneh Soroush Morteza | MAGHOOLI KEIVAN | SETAREHDAN SEYED KAMALEDIN | MOTIE NASRABADI ALI | Issue Writer Certificate

Keywords

Emotion Recognition

Phase Space Reconstruction

Nonlinear EEG Analysis

Committee Machine

Evolutionary Feature Selection

Abstract

Background: Emotion Recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior. Methods: In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of Emotion Recognition. We also take advantage of machine learning methods such as Evolutionary Feature Selection methods and Committee Machines to enhance the classification performance. Classification performed concerning both arousal and valence factors. Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach. Conclusion: Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation.

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APA: Copy

Zangeneh Soroush, Morteza, MAGHOOLI, KEIVAN, SETAREHDAN, SEYED KAMALEDIN, & MOTIE NASRABADI, ALI. (2018). Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods. INTERNATIONAL CLINICAL NEUROSCIENCE JOURNAL, 5(4), 0-0. SID. https://sid.ir/paper/347836/en

Vancouver: Copy

Zangeneh Soroush Morteza, MAGHOOLI KEIVAN, SETAREHDAN SEYED KAMALEDIN, MOTIE NASRABADI ALI. Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods. INTERNATIONAL CLINICAL NEUROSCIENCE JOURNAL[Internet]. 2018;5(4):0-0. Available from: https://sid.ir/paper/347836/en

IEEE: Copy

Morteza Zangeneh Soroush, KEIVAN MAGHOOLI, SEYED KAMALEDIN SETAREHDAN, and ALI MOTIE NASRABADI, “Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods,” INTERNATIONAL CLINICAL NEUROSCIENCE JOURNAL, vol. 5, no. 4, pp. 0–0, 2018, [Online]. Available: https://sid.ir/paper/347836/en

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