Towards Brain-Computer Interfaces for Drone Swarm Control

Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention. Recent advances have been developed for the BCI-based drone control system as the demand for drone control increases. Especially, drone swarm control based on brain signals could provide various industries such as military service or industry disaster. This paper presents a prototype of a brain-swarm interface system for a variety of scenarios using a visual imagery paradigm. We designed the experimental environment that could acquire brain signals under a drone swarm control simulator environment. Through the system, we collected the electroencephalogram (EEG) signals with respect to four different scenarios. Seven subjects participated in our experiment and evaluated classification performances using the basic machine learning algorithm. The grand average classification accuracy is higher than the chance level accuracy. Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.

[1]  Piotr Stawicki,et al.  A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI , 2017, Brain sciences.

[2]  Ji-Hoon Jeong,et al.  Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  João Andrade,et al.  Pure visual imagery as a potential approach to achieve three classes of control for implementation of BCI in non-motor disorders , 2017, Journal of neural engineering.

[4]  John Williamson,et al.  A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[6]  Seong-Whan Lee,et al.  Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[8]  Shuichi Nishio,et al.  BMI control of a third arm for multitasking , 2018, Science Robotics.

[9]  Panagiotis K. Artemiadis,et al.  A hybrid BMI for control of robotic swarms: Preliminary results , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Heung-Il Suk,et al.  Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification , 2013, Neurocomputing.

[11]  Ji-Hoon Jeong,et al.  Trajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  K. Müller,et al.  Effect of higher frequency on the classification of steady-state visual evoked potentials , 2016, Journal of neural engineering.

[13]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[14]  Dinggang Shen,et al.  Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis , 2015, Brain Imaging and Behavior.

[15]  Meng Wang,et al.  A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device , 2018, IEEE Access.

[16]  Bin He,et al.  Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks , 2016, Scientific Reports.

[17]  D. Farina,et al.  Detection of movement intention from single-trial movement-related cortical potentials , 2011, Journal of neural engineering.

[18]  Seong-Whan Lee,et al.  Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Klaus-Robert Müller,et al.  Motion-Based Rapid Serial Visual Presentation for Gaze-Independent Brain-Computer Interfaces , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Panagiotis K. Artemiadis,et al.  On the effect of swarm collective behavior on human perception: Towards brain-swarm interfaces , 2015, 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[21]  Ji-Hoon Jeong,et al.  Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[22]  Kazutaka Ueda,et al.  Development of a Cognitive Brain-Machine Interface Based on a Visual Imagery Method , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[23]  Seong-Whan Lee,et al.  Changes of Functional and Effective Connectivity in Smoking Replenishment on Deprived Heavy Smokers: A Resting-State fMRI Study , 2013, PloS one.

[24]  Hyoung Joong Kim,et al.  A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes , 2016, IEEE Transactions on Information Forensics and Security.

[25]  Ji-Hoon Jeong,et al.  Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).