Disabilities due to neurodegenerative disease are rapidly increasing in number. The need for rehabilitative devices to achieve a normal and comfortable life in the absence of biochannels has also increased. The activities of biochannels can be easily replaced by implementing rehabilitative devices. The electrooculography (EOG)-based human–computer interface (HCI) is one of the most important techniques for enabling disabled persons to enjoy a normal life. The technique of measuring the cornea-retina potential difference is called EOG. The technology for converting thoughts to different control patterns to activate external devices is called the HCI. In this paper, we carried out a study on ten subjects aged 20–30 years using a five-electrode signal acquisition system (AD T26). The subject performances were experimentally verified by implementing periodogram features with a feedforward neural network trained on a nature-inspired algorithm. The analysis was conducted offline and online to evaluate the achievement of the developed HCI. The study showed an average classification accuracy of 93.93% for four tasks, with 95% accuracy for the offline mode and 90.12% for the online mode. The participating subjects drove the mobile robot in all directions frequently and quickly with a recognition accuracy of 98.12%. The study confirmed that the four tasks (related to driving the external device) performed by the subjects were convenient to perform in real time.

神经退行性疾病导致的残疾人数正在迅速增加。在没有生物通道的情况下，对康复设备的需求也增加了，以实现正常和舒适的生活。生物通道的活动可以通过实施康复设备轻松替代。基于眼电图 (EOG) 的人机界面 (HCI) 是使残疾人能够享受正常生活的最重要技术之一。测量角膜-视网膜电位差的技术称为EOG。将思想转换为不同控制模式以激活外部设备的技术称为 HCI。在本文中，我们使用五电极信号采集系统 (AD T26) 对 10 名 20-30 岁的受试者进行了研究。通过使用受自然启发算法训练的前馈神经网络实现周期图特征，对受试者的表现进行了实验验证。分析是离线和在线进行的，以评估开发的 HCI 的成就。研究表明，四个任务的平均分类准确率为 93.93%，离线模式为 95%，在线模式为 90.12%。参与对象频繁快速地向各个方向驾驶移动机器人，识别准确率达到98.12%。研究证实，受试者执行的四项任务（与驱动外部设备有关）便于实时执行。分析是离线和在线进行的，以评估开发的 HCI 的成就。研究表明，四个任务的平均分类准确率为 93.93%，离线模式为 95%，在线模式为 90.12%。参与对象频繁快速地向各个方向驾驶移动机器人，识别准确率达到98.12%。研究证实，受试者执行的四项任务（与驱动外部设备有关）便于实时执行。分析是离线和在线进行的，以评估开发的 HCI 的成就。研究表明，四个任务的平均分类准确率为 93.93%，离线模式为 95%，在线模式为 90.12%。参与对象频繁快速地向各个方向驾驶移动机器人，识别准确率达到98.12%。研究证实，受试者执行的四项任务（与驱动外部设备有关）便于实时执行。