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Open AccessArticle

Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification

1
Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, Germany
2
Wyss-Center for Bio- and Neuro-Engineering, Chemin des Mines 9, Ch 1202 Geneva, Switzerland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(9), 2443; https://doi.org/10.3390/s20092443
Received: 21 February 2020 / Revised: 21 April 2020 / Accepted: 23 April 2020 / Published: 25 April 2020
Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people. View Full-Text
Keywords: electrooculography (EOG); Human-Computer Interface (HCI); Support Vector Machine (SVM) electrooculography (EOG); Human-Computer Interface (HCI); Support Vector Machine (SVM)
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Martínez-Cerveró, J.; Ardali, M.K.; Jaramillo-Gonzalez, A.; Wu, S.; Tonin, A.; Birbaumer, N.; Chaudhary, U. Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification. Sensors 2020, 20, 2443.

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