Real-Time "Eye-Writing" Recognition Using Electrooculogram
- Authors
- Lee, Kwang-Ryeol; Chang, Won-Du; Kim, Sungkean; Im, Chang-Hwan
- Issue Date
- Jan-2017
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Assistive devices; biomedical signal processing; electrooculography (EOG); human-computer interaction (HCI); pattern analysis; rehabilitation
- Citation
- IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.25, no.1, pp.37 - 48
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
- Volume
- 25
- Number
- 1
- Start Page
- 37
- End Page
- 48
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/10550
- DOI
- 10.1109/TNSRE.2016.2542524
- ISSN
- 1534-4320
- Abstract
- Eye movements can be used as alternative inputs for human-computer interface (HCI) systems such as virtual or augmented reality systems as well as new communication ways for patients with locked-in syndrome. In this study, we developed a real-time electrooculogram (EOG)-based eye-writing recognition system, with which users can write predefined symbolic patterns with their volitional eye movements. For the "eye-writing" recognition, the proposed system first reconstructs the eye-written traces from EOG waveforms in real-time; then, the system recognizes the intended symbolic inputs with a reliable recognition rate by matching the input traces with the trained eye-written traces of diverse input patterns. Experiments with 20 participants showed an average recognition rate of 87.38% (F1 score) for 29 different symbolic patterns (26 lower case alphabet characters and three functional input patterns representing Space, Backspace, and Enter keys), demonstrating the promise of our EOG-based eye-writing recognition system in practical scenarios.
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