Today’s life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to measure the horizontal and vertical eye movements. Signals were preprocessed to remove the artifacts and extract the valuable information from collected data by using band power and Hilbert Huang Transform (HHT) and trained with Pattern Recognition Neural Network (PRNN) to classify the tasks. The classification results of 92.17% and 91.85% were shown for band power and HHT features using PRNN architecture. Recognition accuracy was analyzed in offline to identify the possibilities of designing HCI. We compare the two feature extraction techniques with PRNN to analyze the best method for classifying the tasks and recognizing single trail tasks to design the HCI. Our experimental result confirms that for classifying as well as recognizing accuracy of the collected signals using band power with PRNN shows better accuracy compared to other network used in this study. We compared the male subjects performance with female subjects to identify the performance. Finally we compared the male as well as female subjects in age group wise to identify the performance of the system. From that we concluded that male performance was appreciable compared with female subjects as well as age group between 26 to 32 performance and recognizing accuracy were high compared with other age groups used in this study.

当今的生活辅助设备在我们与他人交流中起着重要作用。在这种模式下，基于人机界面（HCI）的眼电图（EOG）发挥着至关重要的作用。通过使用此方法，我们可以在性能和准确性方面克服常规方法。为了克服这样的问题，我们分析了来自二十个受试者的EOG信号，以使用五个电极系统来测量眼睛的水平和垂直运动来设计基于EOG的九种状态的HCI。信号经过预处理以去除伪影，并通过使用带功率和Hilbert Huang变换（HHT）从收集的数据中提取有价值的信息，并通过模式识别神经网络（PRNN）进行训练以对任务进行分类。使用PRNN架构显示的频段功率和HHT特征分类结果分别为92.17％和91.85％。离线分析识别准确性，以确定设计人机交互的可能性。我们将两种特征提取技术与PRNN进行了比较，以分析分类任务和识别单线索任务的最佳方法，以设计HCI。我们的实验结果证实，与本研究中使用的其他网络相比，使用带PRNN的频带功率对收集的信号进行分类和识别的准确性更高。我们将男性受试者的表现与女性受试者进行了比较，以确定其表现。最后，我们在年龄组方面比较了男性和女性受试者，以确定系统的性能。