IEEE Trans Neural Syst Rehabil Eng. 2017 Jan;25(1):37-48. doi: 10.1109/TNSRE.2016.2542524. Epub 2016 Mar 15.
Eye movements can be used as alternative inputs for human-computer interface (HCI) systems such as virtual or augmented reality systems as well as new communication ways for patients with locked-in syndrome. In this study, we developed a real-time electrooculogram (EOG)-based eye-writing recognition system, with which users can write predefined symbolic patterns with their volitional eye movements. For the "eye-writing" recognition, the proposed system first reconstructs the eye-written traces from EOG waveforms in real-time; then, the system recognizes the intended symbolic inputs with a reliable recognition rate by matching the input traces with the trained eye-written traces of diverse input patterns. Experiments with 20 participants showed an average recognition rate of 87.38% (F1 score) for 29 different symbolic patterns (26 lower case alphabet characters and three functional input patterns representing Space, Backspace, and Enter keys), demonstrating the promise of our EOG-based eye-writing recognition system in practical scenarios.
眼球运动可作为人机交互（HCI）系统的替代输入方式，如虚拟或增强现实系统，同时也可为闭锁综合征患者提供新的交流方式。在本研究中，我们开发了一种基于实时眼电图（EOG）的眼写识别系统，用户可以通过自主眼球运动书写预定义的符号模式。对于“眼写”识别，该系统首先从EOG波形中实时重建眼写轨迹；然后，通过将输入轨迹与多种输入模式的训练眼写轨迹进行匹配，以可靠的识别率识别预期的符号输入。对20名参与者进行的实验表明，对于29种不同的符号模式（26个小写字母字符以及代表空格键、退格键和回车键的三种功能输入模式），平均识别率为87.38%（F1分数），这证明了我们基于EOG的眼写识别系统在实际场景中的应用前景。
