A Study on Behavioral Differentiation EEG Data Selecting Algorithm Using LSTM and PCA

Analyzing the EEG signal is a way to use a lot of human body information. Until now, analysis methods using multiple sensors have been used to measure EEG data. Even with a single sensor, effective deep learning results are shown by PCA analysis through data mining. This paper shows the learning data selection algorithm and results for increasing the accuracy of the EEG data information analysis model using deep learning through correlation, data enhancement using PCA technique and GAN. LSTM deep learning model was used for the selection and classification of EEG data signals. In addition, the GAN data learning result provides information on the EEG signal characteristic analysis.