An Efficient Approach to Mental Sentiment Classification with EEG-based Signals Using LSTM Neural Network

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 2K and 1K down to 10 and 15 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 99.43% in EEG emotion state classification and showed an outstanding performance in identifying the mental states with accuracy of 99.27%‎. ‎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.