feature extraction with stacked autoencoders for eeg channel reduction in emotion recognition

Introduction: emotion recognition by electroencephalogram (eeg) signals is one of the complex methods because the extraction and recognition of the features 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 need for research in this field. methods: accordingly, this study investigates the possibility of utilizing deep learning to reduce the number of channels while maintaining the quality of the eeg signal. a stacked autoencoder network extracts optimal features for emotion classification in valence and arousal dimensions. autoencoder networks can extract complex features to provide linear and non- linear features which are a good representative of the signal. results: the accuracy of a conventional emotion recognition classifier (support vector machine) using features extracted from saes was obtained at 75.7% for valence and 74.4% for arousal dimensions, respectively. conclusion: further analysis also illustrates that valence dimension detection with reduced eeg channels has a different composition of eeg channels compared to the arousal dimension. in addition, the number of channels is reduced from 32 to 12, which is an excellent development for designing a small-size eeg device by applying these optimal features.