Research on Emotional Recognition System of EEG Signals Based on CNN+LSTM

Juan Lin. Guoqiang Ma. Binhao Li. Tianyang Yu. Yanjun Li.

DOI: https://doi.org/10.70114/acmsr.2024.1.1.P231

Keywords:

EEG signals; emotion recognition; convolutional neural network; long and short-term memory artificial neural network; Raspberry Pi.

Abstract:

We studied an emotion recognition system based on EEG signals, conducted MATLAB simulation for EEG signal collection, and conducted experiments on EEG signal collection using STM32 microcontroller. We proposed an EEG emotion recognition system based on convolutional neural network (CNN) and short-term memory artificial neural network (LSTM) models. The model used gradient descent algorithm and cross entropy loss function algorithm, and was used the DEAP dataset to verify the accuracy of emotion recognition results, which reached 94.023%, The accuracy of emotion recognition results which is achieved by running the Python trained model on Raspberry Pi reached 98.98%. After compared the data collection information and tested the software and hardware, the experimental results showed that the EEG signal collection system can achieve the functions of EEG signal data collection and improve emotion recognition rate.