EEG-Based Emotion Recognition Using Deep Learning Model for Workers Safet

Abstract

Recently, industrial robots and collaborative robots are widely used in industrial sites with the introduction of smart factory. In a human-robot collaboration environment, it is important to ensure the safety of workers above all. This study suggested an EEG-based deep learning modelbased worker safety management system that guards employees by identifying their feelings when they perceive risk. We evaluated and examined the performance of the suggested CNN, DNN, LSTM, and CNN-LSTM models in order to determine which deep learning model would work best for EEG-based emotion identification. With 71.3% accuracy while utilizing the SEED dataset as input information, the CNN-LSTM model demonstrated good performance; with 74.4% accuracy, the CNN model demonstrated good performance when using the real gathered data set. The proposed deep learning model has a small number of parameters, a small size, and fast processing time, which is advantages for real-time application.