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

By K. Lafleur, K. Cassady, A. Doud, K. Shades, E. Rogin, Bin He. Journal of Neural Engineering, 2013

568 citations recorded by the source corpus.

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain–computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s−1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain–computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0584b4035c7fb4d89793e3dd57d60c24c89adb55

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In Cognitive Neuroscience

Citations in the corpus

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “MI BCIs have also been used to control unmanned ground or aerial vehicles [121, 164, 165], as well as the environment in smart homes [166].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, unknown): “By the same token, BMIs have been now designed to control a large variety of actuators, including computer cursors (114, 283, 463, 692, 725, 794, 864), digital communication systems (10, 71, 101, 247, 545, 559, 672), robotic limbs (114, 152, 360, 463, 795, 817), robotic exoskeletons (156, 456, 467, ”
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, background): “Over the course of numerous BCI studies [16]–[21], growing attention has been dedicated to the analysis of electroencephalography (EEG) signals, especially by movement imagination, called motor imagery (MI) [1], [2], [8], [12], [14], [16], [22].”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, unknown): “In recent years, SMR control signals have been applied to con-trol objects such as quadcopters [39], virtual helicopters [54], and robotic manipulators [20, 47, 55].”
Studies of the Atomic and Crystalline Characteristics of Ceramic Oxide Nano Powders after Bio field Treatment (2015, unknown): “was reported that a robotic quad copter in space can be controlled through the power of thoughts [22].”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, unknown): “3-D movement control [15]–[19], with DOF and performance comparable to studies using intracortical single units recordings [9], [10].”
Biofield Energy Signals, Energy Transmission and Neutrinos (2016, background): “The thoughts are thus a focused beam of neutral neutrinos and can change into positive and negative on interacting with a receiver object composed of atoms and ions and replicate the original signals and thought pattern as observed in BCI and other psychic phenomena.”
A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines (2017, background): “, 2D cursor control [5], wheelchair control [6], and quadcopter control [7], and so on, which are based on motor imagery classification.”
EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks (2015, unknown): “orthoses [46], or quadcopters [16].”