In recent times, the control of human-computer interface (HCI) systems is triggered by electrooculography (EOG) signals. Eye movements recognized based on the EOG signal pattern are utilized to govern the HCI system and do a specific job based on the type of eye movement. With the knowledge of various related examinations, this paper intends a novel model for eye movement recognition based on EOG signals by utilizing Grey Wolf Optimization (GWO) with neural network (NN). Here, the GWO is used to minimize the error function from the classifier. The performance of the proposed methodology was investigated by comparing the developed model with conventional methods. The results reveal the loftier performance of the adopted method with the error minimization analysis and recognition performance analysis in correspondence with varied performance measures such as accuracy, sensitivity, specificity, precision, false-positive rate (FPR), false-negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR) and the F1 score.

中文翻译：

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使用 GWO-NN 优化的基于 EOG 的眼动识别

最近，人机界面 (HCI) 系统的控制由眼电 (EOG) 信号触发。基于 EOG 信号模式识别的眼动用于管理 HCI 系统并根据眼动类型执行特定工作。结合各种相关检查知识，本文利用灰狼优化（GWO）和神经网络（NN），提出了一种基于EOG信号的眼动识别新模型。在这里，GWO 用于最小化分类器的误差函数。通过将开发的模型与传统方法进行比较，研究了所提出方法的性能。1分数。