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A EOG-based switch and its application for “start/stop” control of a wheelchair.

Authors :
Li, Yuanqing
He, Shenghong
Huang, Qiyun
Gu, Zhenghui
Yu, Zhu Liang
Source :
Neurocomputing. Jan2018, Vol. 275, p1350-1357. 8p.
Publication Year :
2018

Abstract

Biological signals, including electroencephalography (EEG) and electrooculography (EOG), are often used to develop switches, which represent a class of typical asynchronous human–computer interfaces (HCIs) in which control and idle states need to be distinguished based on a criterion. Determining a satisfactory criterion for rapid and accurate discrimination between control and idle states remains a challenging issue, as EEG signals are highly noisy and nonstationary, and EOG signals are highly affected by unintended/spontaneous eye movements. Therefore, most existing EEG- or EOG-based switches are characterized by disadvantages of long response times (RTs) or high false positive rates (FPRs). The primary contribution of this work is the development of a novel EOG-based switch design, in which a visual trigger mechanism is introduced to guide the users’ blinks and to assist in detecting blinks. Specifically, the graphical user interface (GUI) includes a switch button that flashes once per 1.2 s. The user is instructed to blink synchronously with the flashes of the switch button to issue an on/off command while a single-channel EOG signal is collected. A waveform detection algorithm is applied to the ongoing EOG signal, which discriminates the intended and unintended blinks mainly based on the synchrony between the blink and the switch buttons flash. Once an intended blink, i.e., a blink corresponding to a button’s flash, is detected, the system issues an on/off command. As one application, the proposed EOG-based switch is used to produce start/stop commands for a wheelchair. Several online experiments were conducted with ten healthy subjects. An average accuracy of 99.5%, an RT of 1.3 s for issuing a switch command in the control state, and an average FPR of 0.10/min in the idle state were achieved. The experimental results therefore demonstrate the effectiveness of the single-channel EOG-based switch. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09252312
Volume :
275
Database :
Academic Search Index
Journal :
Neurocomputing
Publication Type :
Academic Journal
Accession number :
126959091
Full Text :
https://doi.org/10.1016/j.neucom.2017.09.085