State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xian Jiaotong University, Xian, 710049, China.
Comput Intell Neurosci. 2016;2016:4069790. doi: 10.1155/2016/4069790. Epub 2016 Dec 13.
The aim of this study is to build a linear decoding model that reveals the relationship between the movement information and the EOG (electrooculogram) data to online control a cursor continuously with blinks and eye pursuit movements. First of all, a blink detection method is proposed to reject a voluntary single eye blink or double-blink information from EOG. Then, a linear decoding model of time series is developed to predict the position of gaze, and the model parameters are calibrated by the RLS (Recursive Least Square) algorithm; besides, the assessment of decoding accuracy is assessed through cross-validation procedure. Additionally, the subsection processing, increment control, and online calibration are presented to realize the online control. Finally, the technology is applied to the volitional and online control of a cursor to hit the multiple predefined targets. Experimental results show that the blink detection algorithm performs well with the voluntary blink detection rate over 95%. Through combining the merits of blinks and smooth pursuit movements, the movement information of eyes can be decoded in good conformity with the average Pearson correlation coefficient which is up to 0.9592, and all signal-to-noise ratios are greater than 0. The novel system allows people to successfully and economically control a cursor online with a hit rate of 98%.
本研究旨在构建一种线性解码模型，揭示运动信息与 EOG（眼电图）数据之间的关系，以便在眨眼和眼追踪运动的情况下连续在线控制光标。首先，提出了一种眨眼检测方法，以拒绝 EOG 中的自愿单次眨眼或双眨眼信息。然后，开发了一种时间序列的线性解码模型来预测注视位置，并通过 RLS（递归最小二乘）算法校准模型参数；此外，通过交叉验证过程评估解码准确性。此外，还提出了分段处理、增量控制和在线校准来实现在线控制。最后，该技术应用于自愿和在线控制光标以击中多个预定义目标。实验结果表明，眨眼检测算法的自愿眨眼检测率超过 95%，性能良好。通过结合眨眼和平滑追踪运动的优点，眼睛的运动信息可以很好地解码，平均 Pearson 相关系数高达 0.9592，所有信噪比均大于 0。该新系统允许人们以 98%的命中率成功且经济地在线控制光标。
