Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking

Kim, Byung Hyung; Kim, Minho; Jo, Sungho

doi:10.1016/j.compbiomed.2014.04.020

Cited by 101 publications

(55 citation statements)

References 26 publications

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“…By combining different biosignal sensors such as EEG, EMG, and eye tracking, researchers have demonstrated promising success controlling prosthetic limbs and even quadrotor flight [42,25,23,32]. This paper focuses on safetycritical tasks where robot operation must be corrected by a human supervisor with low latency.…”

Section: Hybrid Control Methods For Human-robot Interactionmentioning

confidence: 99%

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

DelPreto

Salazar-Gomez

Gil

et al. 2018

Robotics: Science and Systems XIV

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Abstract-Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rollingwindow classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor's brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows "plug-and-play" operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task.

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Section: Hybrid Control Methods For Human-robot Interactionmentioning

confidence: 99%

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

DelPreto

Salazar-Gomez

Gil

et al. 2018

Robotics: Science and Systems XIV

View full text Add to dashboard Cite

show abstract

“…Finally, Conati and Merten (2007) discuss work they have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy. Kim et al (2014) propose a wearable hybrid interface where eye movements and mental concentration directly influence the control of a quadcopter in three-dimensional space. This noninvasive and low-cost interface addresses limitations of previous work by supporting users to complete their complicated tasks in a constrained environment in which only visual feedback is provided.…”

Section: Intelligent User Interfacementioning

confidence: 99%

“…Based on the visual feedback, the subjects used the interface to navigate along pre-set target locations in the air. The flight performance was evaluated by comparing with a keyboard-based interface (Kim et al 2014).…”

Section: Intelligent User Interfacementioning

confidence: 99%

Introduction to Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents definitions and descriptions of intelligent decision support systems (IDSSs) and analyzes the technology and AI methods, which serve as bases of the IDSS. Scholars have offered various definitions of IDSS. Every one of them accents that an intelligent decision support system is a DSS, which makes extensive use of artificial intelligence techniques. Artificial intelligence techniques can be utilized in all the components of IDSSs, such as in the data base, knowledge base, model base, user interface and the rest. Therefore this chapter deliberates the intelligent databases, hardware (sensors, iris camera hardware, hardware for fingerprint biometric identification, etc.) and computer human interfaces (gesture, intelligent user, motion tracking, voice and natural-language interfaces) in intelligent decision support systems.

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“…In [19,20], the multimodality is used to improve the behavior of an EOG interface (in combination with EEG signals) or a BCI (combined with MEG signals) respectively. EEG was also combined with eye tracking to control a quadcopter [21].…”

Section: Introductionmentioning

confidence: 99%

Combining a Brain–Machine Interface and an Electrooculography Interface to perform pick and place tasks with a robotic arm

Hortal

Íáñez

Úbeda

et al. 2015

Robotics and Autonomous Systems

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Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking

Cited by 101 publications

References 26 publications

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

Introduction to Intelligent Decision Support Systems

Combining a Brain–Machine Interface and an Electrooculography Interface to perform pick and place tasks with a robotic arm

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