ArticleSensors (Basel, Switzerland)2020
Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Denoising eye movement signals using a novel signal processing framework to preserve oculomotor integrity in dyslexia.Scientific reports · 2026Article
- Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.Scientific reports · 2024Article
- A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography.Sensors (Basel, Switzerland) · 2024Article
- Biosensor Development and Innovation in Healthcare and Medical Applications.Sensors (Basel, Switzerland) · 2023Article
- Measuring Biosignals with Single Circuit Boards.Bioengineering (Basel, Switzerland) · 2022Review
- Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.Sensors (Basel, Switzerland) · 2021Review
- Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot.Sensors (Basel, Switzerland) · 2021Article
- Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM.Biosensors · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.