A Single-Channel EOG-Based Speller
| Autor: | Yuanqing Li, Shenghong He |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male Support Vector Machine Eye Movements Computer science Feature vector Interface (computing) Speech recognition 0206 medical engineering Feature extraction Wavelet Analysis Biomedical Engineering 02 engineering and technology Communication Aids for Disabled User-Computer Interface Young Adult 03 medical and health sciences 0302 clinical medicine Mode (computer interface) Internal Medicine medicine Humans Waveform Computer vision Graphical user interface Blinking medicine.diagnostic_test business.industry General Neuroscience Rehabilitation Signal Processing Computer-Assisted Equipment Design Electrooculography 020601 biomedical engineering Healthy Volunteers Support vector machine Calibration Female Artificial intelligence Energy Metabolism business Algorithms Psychomotor Performance 030217 neurology & neurosurgery |
| Zdroj: | IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25:1978-1987 |
| ISSN: | 1558-0210 1534-4320 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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