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Emotion recognition by EEG signals is one of the complex methods because the extraction and recognition of the features that are hidden in the signal are sophisticated and require a significant number of EEG channels. Presenting a method for feature analysis and an algorithm for reducing the number of EEG channels fulfills the needs for research in this field. Therefore, this study has investigated the possibility of utilizing deep learning to reduce the number of channels while maintaining the quality of the EEG signal. Stacked autoencoder network (SAEs) is used to extract optimal features for the classification of emotion in valence and arousal dimensions. Autoencoder networks have the ability to extract complex features to provide linear and non-linear features which are a good representative of the signal.  The accuracy of conventional emotion recognition classifier (SVM) using feature extracted from SAEs was obtained 75.7% for valence and 74.4% for arousal dimensions, respectively. Further analysis also illustrates that valence dimension detection with reduced EEG channels has a different composition of EEG channels compared to arousal dimension. In addition, the number of channels is reduced from 32 to 12, which is an excellent development to design a small size EEG device by applying these optimal features.
نوع مطالعه: Original | موضوع مقاله: Cognitive Neuroscience
دریافت: 1401/10/18 | پذیرش: 1401/12/1

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