An Efficient Approach to Mental Sentiment Classification with EEG-based Signals Using LSTM Neural Network
عنوان مقاله: An Efficient Approach to Mental Sentiment Classification with EEG-based Signals Using LSTM Neural Network
شناسه ملی مقاله: JR_COAM-6-1_004
منتشر شده در در سال 1400
شناسه ملی مقاله: JR_COAM-6-1_004
منتشر شده در در سال 1400
مشخصات نویسندگان مقاله:
Ali Badie - Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
Mohammad Amin Moragheb - Department of Computer Engineering, Mamasani Higher Education Center, Mamasani, Iran
Ali Noshad - Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
خلاصه مقاله:
Ali Badie - Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
Mohammad Amin Moragheb - Department of Computer Engineering, Mamasani Higher Education Center, Mamasani, Iran
Ali Noshad - Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
This research explores the prominent signals and presents an effective approach to identify emotional experiences and mental states based on EEG signals. First, PCA is used to reduce the data's dimensionality from ۲K and ۱K down to ۱۰ and ۱۵ while improving the performance. Then, regarding the insufficient high-quality training data for building EEG-based recognition methods, a multi-generator conditional GAN is presented for the generation of high-quality artificial data that covers a more complete distribution of actual data by utilizing different generators. Finally, to perform classification, a new hybrid LSTM-SVM model is introduced. The proposed hybrid network attained overall accuracy of ۹۹.۴۳% in EEG emotion state classification and showed an outstanding performance in identifying the mental states with accuracy of ۹۹.۲۷%. The introduced approach successfully combines two prominent targets of machine learning: high accuracy and small feature size, and demonstrates a great potential to be utilized in future classification tasks.
کلمات کلیدی: EEG, GANs, LSTM Networks, Biomedical signal processing, Deep learning
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1605891/