Emotional Monitoring of Learners Based on EEG Signal Recognition
Ludi Bai, Junqi Guo, Tianyou Xu, Minghui Yang
Abstract
The importance of education determines learners’ learning attributes. In the process of learning, the attitude of learning greatly affects the efficiency and direction of their learning. The emotion of the learner is an important expression of the attitude of learning, so it is very practical to grasp the positive or negative emotion of the students in real time. This article is based on this direction. We combine EEG signals, emotion detection and RNN cyclic neural networks, and use the sequence classification to identify the talented RNN variant network LSTM with long-term memory. It will improve the accuracy of emotional monitoring based on EEG signals, and thus improve the feasibility of monitoring learners’ emotional enthusiasm in reality.