Abstract
There is a considerable number of people with some disability they may go from partial limb disability to total incapacity. For these people, technology is an opportunity to bring them back some capabilities. In this work, we present a framework where we envisage a system that can be used by a disabled person who can not move but still possesses eye movements. Therefore, by using electroencephalographic (EEG) signals, we recognize and classify eye movements, which are then translated to control commands. Based on the latter, we developed an application to illustrate how such commands could be used to control a drone that could be used to deliver messages or carry out any other activity that involves the drone having to fly from a start point to a final destination. The results obtained in this study indicate that ocular movements are recognized with an accuracy of 86%, which suggests the feasibility of our approach.
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This work has been partially funded by the Royal Society through the Newton Advanced Fellowship with reference NA140454.
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Corichi, E.Z., Carranza, J.M., García, C.A.R., Pineda, L.V. (2018). A Framework Based on Eye-Movement Detection from EEG Signals for Flight Control of a Drone. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_28
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