A quadcopter controlled by brain concentration and eye blink

Song, Xiaomu; Mann, Keith O.; Allison, Eric; Yoon, Suk-Chung; Hila, Henri; Müller, Albert; Gieder, Christine

doi:10.1109/spmb.2016.7846875

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…A possible approach to address this issue comes from brain–computer interfaces (BCIs), which bypass behavioral output by directly retrieving the desired information from the cerebral activity patterns, often relying on mental imagery. Successful examples include the control of humanoids ( 18 ), unmanned aerial vehicles (UAVs) ( 19 – 21 ), wheelchairs, and telepresence systems for motion-impaired individuals ( 9 – 12 ). BCIs do nonetheless come with certain limitations, which may prevent their widespread utilization.…”

mentioning

confidence: 99%

Data-driven body–machine interface for the accurate control of drones

Miehlbradt

Cherpillod

Mintchev

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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SignificanceThe teleoperation of nonhumanoid robots is often a demanding task, as most current control interfaces rely on mappings between the operator’s and the robot’s actions, which are determined by the design and characteristics of the interface, and may therefore be challenging to master. Here, we describe a structured methodology to identify common patterns in spontaneous interaction behaviors, to implement embodied user interfaces, and to select the appropriate sensor type and positioning. Using this method, we developed an intuitive, gesture-based control interface for real and simulated drones, which outperformed a standard joystick in terms of learning time and steering abilities. Implementing this procedure to identify body-machine patterns for specific applications could support the development of more intuitive and effective interfaces.

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mentioning

confidence: 99%

Data-driven body–machine interface for the accurate control of drones

Miehlbradt

Cherpillod

Mintchev

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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“…The main purpose of this research was to help people who lost their limbs due to an accident. A BCI system was developed in [6] to control the movement of a quadcopter by identified brain concentration and eye blink. EEG data was captured by Emotiv Epoc headset and sent to data processing computer to analyze it in real time.…”

Section: Related Workmentioning

confidence: 99%

“…The signal is then sampled at rate 128 Hz, and bandpass filtered between 0.2 Hz and 43 Hz. Power-line noise at 50 Hz and 60 Hz are also attenuated before the data are sent to the wireless transmitter [6]. Fig.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Electroencephalography Signals Based Face Interaction for Directional Control System

Al-Qaraawi¹,

Croock²,

Alawi³

2018

IJCNT

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Recently, the Electroencephalography (EEG) signals of human brain are considered by researchers for performing different applications that can solve numerous problems. These research works tackle the problems of movements for sever disabled people. In this paper, a directional control system based on EEG signals of human brain is proposed. The EEG signals are read using a Five Channels Emotiv headset that includes seven nodes. The read EEG signals are sent using WiFi technique to the Raspberry Pi III for processing and producing directional control signals. The four movement directions are represented by indicators as a prototype to simulate the direction control, as well as the ON/OFF signal as a master key. Different case studies are considered to test the proposed system over the expected circumstances.

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“…A flight computer or controller can be applied to convert the operator's commands into the desired motion. Quadcopters can be equipped with various electromechanical devices (e.g., sensor and camera) to gather the data on phenomena in urban areas [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Urban Monitoring System by Using a Quadcopter and Intelligent Learning Techniques

Khanmohammadi

Samadi

2023

JAMRIS

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The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve the flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. Main objectives of this system are to monitor the climate status, temperature, humidity, and smoke as well as to detect the fire occurrences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the pre-determined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status, effects of the sensors’ data changes on climate status. An illustrative example is used to draw the simulated area by RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices.

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A quadcopter controlled by brain concentration and eye blink

Cited by 7 publications

References 10 publications

Data-driven body–machine interface for the accurate control of drones

Data-driven body–machine interface for the accurate control of drones

Electroencephalography Signals Based Face Interaction for Directional Control System

A Cloud-Based Urban Monitoring System by Using a Quadcopter and Intelligent Learning Techniques

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