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Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface are intensified in a random order. To select a button, the user must blink his/her eyes in synchrony as the target button is flashed. Two data processing procedures, specifically support vector machine (SVM) classification and waveform detection, are combined to detect eye blinks. During detection, we simultaneously feed the feature vectors extracted from the ongoing EOG signal into the SVM classification and waveform detection modules. Decisions are made based on the results of the SVM classification and waveform detection. Three online experiments were conducted with eight healthy subjects. We achieved an average accuracy of 94.4% and a response time of 4.14 s for selecting a character in synchronous mode, as well as an average accuracy of 93.43% and a false positive rate of 0.03/min in the idle state in asynchronous mode. The experimental results, therefore, demonstrated the effectiveness of this single-channel EOG-based speller.

Original publication

DOI

10.1109/TNSRE.2017.2716109

Type

Journal article

Journal

IEEE Trans Neural Syst Rehabil Eng

Publication Date

11/2017

Volume

25

Pages

1978 - 1987

Keywords

Adult, Algorithms, Blinking, Calibration, Communication Aids for Disabled, Electrooculography, Energy Metabolism, Equipment Design, Eye Movements, Female, Healthy Volunteers, Humans, Male, Psychomotor Performance, Signal Processing, Computer-Assisted, Support Vector Machine, User-Computer Interface, Wavelet Analysis, Young Adult