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Low-Cost Brain-Computer Interface System for AR Drone Control

Author: Rafael Mendes Duarte
Orientador: Prof. Alexandre Trofino Neto, PhD.


“This work presents the design, implementation, and testing of a Brain-Computer Interface (BCI) system based on µ-waves to control the navigation of a drone. BCI systems perform the translation of brain signals into commands to communicate with external applications. The µ rhythm is a type of brain signal response to motor activity which can be easily measured by electroencephalography (EEG). For this reason, µ-waves based BCI systems have been extensively explored in the lit- erature as a way of enabling patients with compromised neuromotor functions to interact with the outside world. To implement the sig- nal processing and application interface routines, a software platform was built based on well-established filter and classification techniques, such as the Common Spatial Patterns (CSP) and the Linear Discrimi- nant Analysis (LDA). For interfacing with the drone, an algorithm for translating the classifier outputs into drone commands was proposed. In addition, the acquisition of brain waves was performed by a low-cost and open-hardware EEG amplifier called OpenBCI. The validation of the designed system was performed using public and an acquired motor imagery EEG datasets, which were supplied to the platform to simu- late the real-time performance of the system. The tests, conducted in a drone simulator, demonstrated the correct operation of the proposed methodology and the designed system.”

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