Towards Brain-Computer Interfaces for Drone Swarm Control

Jeong, Ji-Hoon; Lee, Dae-Hyeok; Ahn, Hyung-Ju; Lee, Seong–Whan

doi:10.1109/bci48061.2020.9061646

Cited by 25 publications

(14 citation statements)

References 23 publications

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“…For this reason, the dual-task paradigm has been widely adopted [29], [30], [31] to simulate the highly cognitive-demanding operation scenarios in real world, where the performance measurements of the primary task reflect the operator's manipulation ability under high cognitive load, and those of a secondary task provide objective information about the operator's cognitive load. Most previous studies have investigated the BCI-based and gaze-based control performance in the singletask paradigm [7], [8], [9], [32], [33], [34], [35], whereas few efforts have been made in a more demanding dual-task paradigm where the operator's both hands are involved until very recently.…”

Section: B Bci/gaze-based Input Under Dual-taskingmentioning

confidence: 99%

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Zeng

Shen

Sun

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Currently there still remains a critical need of human involvements for multi-robot system (MRS) to successfully perform their missions in real-world applications, and the hand-controller has been commonly used for the operator to input MRS control commands. However, in more challenging scenarios involving concurrent MRS control and system monitoring tasks, where the operator's both hands are busy, the hand-controller alone is inadequate for effective human-MRS interaction. To this end, our study takes a first step toward a multimodal interface by extending the hand-controller with a hands-free input based on gaze and brain-computer interface (BCI), i.e., a hybrid gaze-BCI. Specifically, the velocity control function is still designated to the hand-controller that excels at inputting continuous velocity commands for MRS, while the formation control function is realized with a more intuitive hybrid gaze-BCI, rather than with the hand-controller via a less natural mapping. In a dual-task experimental paradigm that simulated the hands-occupied manipulation condition in real-world applications, operators achieved improved performance for controlling simulated MRS (average formation inputting accuracy increases 3%, average finishing time decreases 5 s), reduced cognitive load (average reaction time for secondary task decreases 0.32 s) and perceived workload (average rating score decreases 15.84) with the hand-controller extended by the hybrid gaze-BCI, over those with the hand-controller alone. These findings reveal the potential of the hands-free hybrid gaze-BCI to extend the traditional manual MRS input devices for creating a more operator-friendly interface, in challenging hands-occupied dual-tasking scenarios.

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Section: B Bci/gaze-based Input Under Dual-taskingmentioning

confidence: 99%

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Zeng

Shen

Sun

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Early attempts have already started to emerge. BCIs have been applied to robot control [ 10 ], smart home [ 11 ], disaster management [ 12 ], and multimedia interactions [ 13 ]. For example, in [ 14 ], the authors proposed to use a P300-based BCI to switch various household appliances on and off.…”

Section: Introductionmentioning

confidence: 99%

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis

Wang

et al. 2022

Sensors

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Background: The brain–computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers. Methods: In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system. Conclusion: The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.

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“…Recently, non-invasive BCI systems for commercial and military applications are being actively researched. One of the most challenging research topics is collaborating with a swarm of robots or drones using electroencephalogram (EEG) signals [4], [12], [13]. Applications of drone swarm have great potentials for both military and civilian usages.…”

Section: Introductionmentioning

confidence: 99%

Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms

Lee

Jeong

Ahn

et al. 2021

2021 9th International Winter Conference on Brain-Computer Interface (BCI)

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Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artificial intelligence, there have been many developments in the drone control system. BCI characteristic that can reflect the users' intentions led to the BCI-based drone control system. When using drone swarm, we can have more advantages, such as mission diversity, than using a single drone. In particular, BCI-based drone swarm control could provide many advantages to various industries such as military service or industry disaster. BCI Paradigms consist of the exogenous and endogenous paradigms. The endogenous paradigms can operate with the users' intentions independently of any stimulus. In this study, we designed endogenous paradigms (i.e., motor imagery (MI), visual imagery (VI), and speech imagery (SI)) specialized in drone swarm control, and EEG-based various task classifications related to drone swarm control were conducted. Five subjects participated in the experiment and the performance was evaluated using the basic machine learning algorithm. The grand-averaged accuracies were 51.1% (± 8.02), 53.2% (± 3.11), and 41.9% (± 6.09) in MI, VI, and SI, respectively. Hence, we confirmed the feasibility of increasing the degree of freedom for drone swarm control using various endogenous paradigms.

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Towards Brain-Computer Interfaces for Drone Swarm Control

Cited by 25 publications

References 23 publications

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis

Design of an EEG-based Drone Swarm Control System using Endogenous BCI Paradigms

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