Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

Karl LaFleur; Kaitlin Cassady; Alexander Doud; Kaleb Shades; Eitan Rogin; Bin He

doi:10.1088/1741-2560/10/4/046003

Journal of Neural Engineering

Paper • The following article is Open access

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

Karl LaFleur¹, Kaitlin Cassady¹, Alexander Doud¹, Kaleb Shades¹, Eitan Rogin¹ and Bin He^3,1,2

Published 4 June 2013 • © 2013 IOP Publishing Ltd
Journal of Neural Engineering, Volume 10, Number 4 Citation Karl LaFleur et al 2013 J. Neural Eng. 10 046003 DOI 10.1088/1741-2560/10/4/046003

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Supplementary data

Supplementary data. (152 KB, PDF)

Supplementary data video 1. (2.01 MB, WMV) Subject 1 acquires two targets in 30 seconds. The subject actuates left and right turning to approach the rings from behind in order to be correctly oriented to make a pass at the next ring.

Supplementary data video 2. (1.42 MB, WMV) Subject 2 shows the ability to suppress left and right actuation in order to take the most direct path between the two rings. This ability is assisted by the nonlinear control algorithm.

Supplementary data video 3. (1.22 MB, WMV) Subject 3 acquires two targets consecutively within 20 seconds. The subject shows off an ability to indirectly modulate velocity between rings (slowing down in this case) with sudden left or right turns that shift momentum.

Supplementary data video 4. (1.45 MB, WMV) Subject 4 demonstrates both continuous and rapid control of the quadcopter by successfully achieving two target acquisitions. Note that the two target acquisitions were differentiated from each other by a period of 10 seconds, which is greater than the predefined threshold for a new target acquisition event.

Supplementary data video 5. (1.47 MB, WMV) Subject 5 immediately acquires a ring after take-off, demonstrating fast acclimation to the BCI system when beginning a new run.

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

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