Gorur Kutlucan, Bozkurt M Recep, Bascil M Serdar, Temurtas Feyzullah
Department of Electrical and Electronics Engineering, Sakarya University, 54187, Sakarya, Turkey.
Department of Electrical and Electronics Engineering, Bozok University, 66200, Yozgat, Turkey.
Australas Phys Eng Sci Med. 2018 Jun;41(2):379-391. doi: 10.1007/s13246-018-0635-x. Epub 2018 Apr 9.
The tongue is an aesthetically useful organ located in the oral cavity. It can move in complex ways with very little fatigue. Many studies on assistive technologies operated by tongue are called tongue-human computer interface or tongue-machine interface (TMI) for paralyzed individuals. However, many of them are obtrusive systems consisting of hardware such as sensors and magnetic tracer placed in the mouth and on the tongue. Hence these approaches could be annoying, aesthetically unappealing and unhygienic. In this study, we aimed to develop a natural and reliable tongue-machine interface using solely glossokinetic potentials via investigation of the success of machine learning algorithms for 1-D tongue-based control or communication on assistive technologies. Glossokinetic potential responses are generated by touching the buccal walls with the tip of the tongue. In this study, eight male and two female naive healthy subjects, aged 22-34 years, participated. Linear discriminant analysis, support vector machine, and the k-nearest neighbor were used as machine learning algorithms. Then the greatest success rate was achieved an accuracy of 99% for the best participant in support vector machine. This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be alternative control and communication channel for traditional electroencephalography (EEG)-based brain-computer interfaces which have significant inadequacies arisen from the EEG signals.
舌头是位于口腔内具有美学用途的器官。它能够以复杂的方式运动且很少产生疲劳。许多关于由舌头操作的辅助技术的研究被称为舌头-人机接口或舌头-机器接口（TMI），用于瘫痪个体。然而，其中许多都是侵入性系统，由放置在口腔和舌头上的传感器和磁性追踪器等硬件组成。因此，这些方法可能会令人厌烦、缺乏美感且不卫生。在本研究中，我们旨在通过研究机器学习算法在基于舌头的一维控制或辅助技术通信方面的成功情况，仅使用舌动电位开发一种自然且可靠的舌头-机器接口。舌动电位响应是通过用舌尖触碰颊壁产生的。在本研究中，有8名年龄在22至34岁之间的未接触过相关实验的健康男性和2名健康女性参与。线性判别分析、支持向量机和k近邻算法被用作机器学习算法。然后，在支持向量机中，最佳参与者实现了99%的准确率，获得了最高成功率。本研究可为残疾人以自然、非侵入性、快速且可靠的方式控制辅助设备提供帮助。此外，预计基于舌动电位的TMI可以成为传统基于脑电图（EEG）的脑机接口的替代控制和通信渠道，而脑电图信号存在明显不足。
