Affiliation:
1. College of Computer and Information Engineering Nanjing Tech University Nanjing Jiangsu China
2. Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering Southeast University Nanjing Jiangsu China
Abstract
In this study, a prediction model for visual fatigue is developed. As input, frequential and nonlinear features are extracted from multichannel EEG, and then dimensionally reduced. In the model, bidirectional LSTM and attention layers are combined for effective learning. As a result, 82.90% accuracy, 85.26% weighted precision, 82.90% weighted recall, and 84.02% weighted F1‐score were obtained.