Literature DB >> 28641264

A Single-Channel EOG-Based Speller.

Shenghong He, Yuanqing Li.   

Abstract

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.

Entities:  

Mesh:

Year:  2017        PMID: 28641264     DOI: 10.1109/TNSRE.2017.2716109

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  7 in total

Review 1.  EOG-Based Human-Computer Interface: 2000-2020 Review.

Authors:  Chama Belkhiria; Atlal Boudir; Christophe Hurter; Vsevolod Peysakhovich
Journal:  Sensors (Basel)       Date:  2022-06-29       Impact factor: 3.847

2.  Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces.

Authors:  Francisco Laport; Daniel Iglesia; Adriana Dapena; Paula M Castro; Francisco J Vazquez-Araujo
Journal:  Sensors (Basel)       Date:  2021-03-22       Impact factor: 3.576

3.  Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram.

Authors:  M Thilagaraj; B Dwarakanath; S Ramkumar; K Karthikeyan; A Prabhu; Gurusamy Saravanakumar; M Pallikonda Rajasekaran; N Arunkumar
Journal:  J Healthc Eng       Date:  2021-12-08       Impact factor: 2.682

4.  A Human-Machine Interface Based on an EOG and a Gyroscope for Humanoid Robot Control and Its Application to Home Services.

Authors:  Fan Wang; Xiongzi Li; Jiahui Pan
Journal:  J Healthc Eng       Date:  2022-03-19       Impact factor: 2.682

5.  Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals.

Authors:  Xugang Cai; Jiahui Pan
Journal:  J Healthc Eng       Date:  2022-04-18       Impact factor: 3.822

6.  A novel brain-controlled wheelchair combined with computer vision and augmented reality.

Authors:  Kaixuan Liu; Yang Yu; Yadong Liu; Jingsheng Tang; Xinbin Liang; Xingxing Chu; Zongtan Zhou
Journal:  Biomed Eng Online       Date:  2022-07-26       Impact factor: 3.903

7.  Face-Computer Interface (FCI): Intent Recognition Based on Facial Electromyography (fEMG) and Online Human-Computer Interface With Audiovisual Feedback.

Authors:  Bo Zhu; Daohui Zhang; Yaqi Chu; Xingang Zhao; Lixin Zhang; Lina Zhao
Journal:  Front Neurorobot       Date:  2021-07-16       Impact factor: 2.650

  7 in total

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