Study for Electrooculography Character Input Based on Dual-Channel CNN of Movement Disorder Patients
Yi Ding, Pusheng Tang, Jinrong Li, Cheng Zeng, Yongzhi Sun, Yong Zhang, Wei Liu · IEEE Access · 2024
Movement disorder patients are usually unable to easily communicate with the outside world. Therefore, a novel approach for EOG character input based on DC-CNN is proposed in this paper. Users only need to control the eyeball movement and eye blinking so as to manipulate the interactive interface on the screen for character input. The signal acquisition device used in this study adopts a dual-channel acquisition method. The EOG signals are preprocessed, converted to digital signals, and filtered for noise using bandpass filtering and wavelet transform. The filtered and denoised signals are input into the proposed novel approach for training the classification model. During the real-time interaction, the EOG signals are input into the trained model for classification. Next, the classification result is used as the input signal for a virtual keyboard which is compiled by PYQT5. After that, the virtual keyboard can generate corresponding responses depending on different input signals to achieve the character input. The model achieved a high classification accuracy of 99.3% during training and 98.5% in real-time classification, significantly outperforming existing methods. Furthermore, the accuracy of single character input and paragraph input can arrive at 98.2% and 97.6% as well, respectively. This novel DC-CNN approach offers a reliable and user-friendly communication method for movement disorder patients, enhancing their ability to interact with the world.