The Faculty of Social development and Western China Development Studies, Sichuan University, Chengdu, 610065, China; School of Business, Sichuan University, Chengdu, 610065, China.
School of Business, Sichuan University, Chengdu, 610065, China.
Artif Intell Med. 2020 Jan;102:101765. doi: 10.1016/j.artmed.2019.101765. Epub 2019 Nov 21.
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.
如今，生活辅助设备在我们的生活中扮演着重要的角色，帮助我们与他人进行交流。在这种模式下，基于人机接口（HCI）的眼电图（EOG）发挥着至关重要的作用。通过使用这种方法，我们可以克服传统方法在性能和准确性方面的局限性。为了解决这个问题，我们分析了来自二十名受试者的 EOG 信号，设计了一种基于 EOG 的九状态 HCI，使用五个电极系统来测量水平和垂直眼球运动。我们对信号进行预处理，以去除伪影，并通过带功率和希尔伯特黄变换（HHT）从采集的数据中提取有价值的信息，然后使用模式识别神经网络（PRNN）对任务进行分类。使用 PRNN 架构，带功率和 HHT 特征的分类结果分别为 92.17%和 91.85%。离线分析识别精度，以确定设计 HCI 的可能性。我们将这两种特征提取技术与 PRNN 进行比较，以分析用于分类任务和识别单轨迹任务的最佳方法，从而设计 HCI。我们的实验结果证实，对于使用带功率和 PRNN 进行分类以及识别采集信号的准确性，与本研究中使用的其他网络相比，带功率显示出更好的准确性。我们比较了男性和女性受试者的性能，以确定性能。最后，我们在年龄组内比较了男性和女性受试者，以确定系统的性能。从这些结果中，我们得出结论，与女性受试者以及年龄在 26 至 32 岁之间的受试者相比，男性受试者的表现更为出色，并且与本研究中使用的其他年龄组相比，识别准确性更高。
