Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification
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 direct...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/9/2443 |
| _version_ | 1797569660898508800 |
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| author | Jayro Martínez-Cerveró Majid Khalili Ardali Andres Jaramillo-Gonzalez Shizhe Wu Alessandro Tonin Niels Birbaumer Ujwal Chaudhary |
| author_facet | Jayro Martínez-Cerveró Majid Khalili Ardali Andres Jaramillo-Gonzalez Shizhe Wu Alessandro Tonin Niels Birbaumer Ujwal Chaudhary |
| author_sort | Jayro Martínez-Cerveró |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:14:44Z |
| format | Article |
| id | doaj.art-b2531b4c609248fcb2f6ed3ff2e80c26 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:14:44Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b2531b4c609248fcb2f6ed3ff2e80c262023-11-19T22:40:58ZengMDPI AGSensors1424-82202020-04-01209244310.3390/s20092443Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal ClassificationJayro Martínez-Cerveró0Majid Khalili Ardali1Andres Jaramillo-Gonzalez2Shizhe Wu3Alessandro Tonin4Niels Birbaumer5Ujwal Chaudhary6Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, GermanyInstitute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, GermanyInstitute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, GermanyInstitute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, GermanyWyss-Center for Bio- and Neuro-Engineering, Chemin des Mines 9, Ch 1202 Geneva, SwitzerlandInstitute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, GermanyInstitute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstraße 5, 72076 Tübingen, GermanyElectrooculography (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.https://www.mdpi.com/1424-8220/20/9/2443electrooculography (EOG)Human-Computer Interface (HCI)Support Vector Machine (SVM) |
| spellingShingle | Jayro Martínez-Cerveró Majid Khalili Ardali Andres Jaramillo-Gonzalez Shizhe Wu Alessandro Tonin Niels Birbaumer Ujwal Chaudhary Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification Sensors electrooculography (EOG) Human-Computer Interface (HCI) Support Vector Machine (SVM) |
| title | Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification |
| title_full | Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification |
| title_fullStr | Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification |
| title_full_unstemmed | Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification |
| title_short | Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification |
| title_sort | open software hardware platform for human computer interface based on electrooculography eog signal classification |
| topic | electrooculography (EOG) Human-Computer Interface (HCI) Support Vector Machine (SVM) |
| url | https://www.mdpi.com/1424-8220/20/9/2443 |
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