英文摘要 | In recent years, with the rapid development of stereoscopic display technology, stereoscopic display applications such as 3DTV have entered people’s life. Compared with the ordinary 2D display technology, 3D display can provide a more realistic visual experience. However, with the application range of stereoscopic display technology getting wider, the problem of visual fatigue caused by the conflict between its imaging principle and the human visual system is also becoming increasing prominent and gradually restricts the development of 3D display technology. Due to current technical limitations, health problems such visual fatigue caused by viewing 3D content cannot be eliminated. Therefore, it is important to make a reasonable and accurate assessment of the level of the visual fatigue of users to reduce the negative effects brought by the 3D technology and enhance the user experience.
In this paper, EEG signals are adopted as objective assessment indicators and deep learning is used as modeling methods to study the problem of visual fatigue assessment. Aiming at the problem of traditional visual fatigue assessment and considering the characteristics of EEG, an end-to-end visual fatigue assessment method is proposed. The application of deep learning methods in visual fatigue assessment is explored. In addition, problem of insufficient labeled data in the classification task of EEG signals is discussed in this paper and a solution based on multi-task learning is proposed. The key points of this paper are as follows:
1. Based on the characteristics of EEG signals, a deep learning model DeepFatigueNet is proposed for EEG classification task. The method is inspired by the traditional feature extraction algorithms for EEG signals. The temporal and spatial features of EEG signals are extracted by a specially designed convolutional block and classified. The whole process is carried out in an end-to-end learning manner, avoiding the complicated and time-consuming traditional feature extraction process.
2. Aiming at the problem of lacking of large-scale labeled data for EEG classification task, a deep learning method based on multi-task learning framework is proposed. This method consists of three parts, namely representation learning module, classification module and reconstruction module. Based on the multi-task learning framework, the three modules are optimized in an end-to-end manner. This method can further improve the performance of deep learning models when there is limited labeled data.
3. Apply deep learning methods to visual fatigue assessment. Based on the visual fatigue assessment dataset, DeepFatigueNet is compared with traditional machine learning method and other deep learning structures. The experimental results show that DeepFatigueNet outperforms other methods, which demonstrates the potential of deep learning in visual fatigue assessment and the effectiveness of our model.
4. Apply multi-task learning model to the visual fatigue assessment and motor imagery (MI) classification. Based on the visual fatigue assessment dataset and public dataset BCI Competition IV dataset 2a for MI, the multi-task learning model is compared with state-of-the-art deep learning methods. The experimental results show that the problem of insufficient data does limit the improvement of performance of deep learning methods. Moreover, our proposed multi-task learning method can further boost the performance of deep learning models with limited data. The above results demonstrate that our proposed multi-task learning method is effective and generic. |
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