Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI

Stawicki, Piotr; Gembler, Felix; Volosyak, Ivan

doi:10.1155/2016/4909685

Cited by 47 publications

(21 citation statements)

References 40 publications

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“…LEDs were used to generate the stimuli. Similarly, the BCI system proposed by Stawicki et al (2016) has live video feedback and allows to control the camera and MRC. When you are in driver mode, you have the options to go forward, turn left, turn right and change to camera mode; in camera mode, you have the possibility to look to the left, look to the right, look up and change to driver mode.…”

Section: Unmanned Ground Vehiclesmentioning

confidence: 99%

“…It reflects that among the 40 reviewed articles, only Liu et al (2018) does not show concise information in this aspect. The research in which the largest number of experimental subjects has participated was in Stawicki et al (2016) with 61 people and it was the article that obtained one of the best results (with an accuracy of 93.03%). The articles that have tested with the smallest number of subjects were in Gonzalez-Mendoza et al (2015) and Yang et al (2017) with 2 people each.…”

Section: Analysis Of Technical Specifications Of Previous Work Methomentioning

confidence: 99%

“…Applicability of SSVEP-based brain-computer interfaces for robot navigation Success rate in real environments Lee et al (2012) A brain-wave-actuated small robot car using ensemble empirical mode Command transfer interval, decomposition-based approach information transfer rate and success rate Li et al (2017) A human-vehicle collaborative simulated driving system based on hybrid T-test, success rate and time brain-computer interfaces and computer vision efficiency Liu et al (2018) Design of a video feedback SSVEP-BCI system for car control based on Success rate improved MUSIC method Turnip et al (2016) An application of online ANFIS classifier for wheelchair based brain computer Success rate interface Wang et al (2018) A wearable SSVEP-based BCI system for quadcopter control using Online accuracy, success rate, head-mounted device t i m e e f f i c i e n c y a n d information transfer rate Mistry et al (2018) An SSVEP based brain computer interface system to control electric wheelchairs Success rate and time efficiency Nakanishi et al (2017) Enhancing detection of SSVEPs for a high-speed brain speller using CCA extended task-related component analysis Pelayo et al (2018) Brain-computer interface controlled robotic arm to improve quality of life Success rate Peng et al (2016) Control of a nursing bed based on a hybrid brain-computer interface Success rate, time efficiency and false-positive rate Perera et al (2017) EEG-controlled meal assistance robot with camera-based automatic mouth position Efficiency of time tracking and mouth open detection Perera et al (2017) SSVEP Based BMI for a meal assistance robot Success rate and time efficiency Zhao et al 2017A SSVEP intelligent home service system based on CCA Success rate Ruhunage et al (2017) EMG signal controlled transhumeral prosthetic with EEG-SSVEP based Success rate and time efficiency approach for hand open/close Savic and Popovic (2015) Brain computer interface prototypes for upper limb rehabilitation: a review of principles and experimental results Success rate and time efficiency Stawicki et al (2016) Driving a semiautonomous mobile robotic car controlled by an SSVEP-Based BCI Success rate and information transfer rate Su and Li (2017) Brain-computer interface based stochastic navigation and control of a ERA, success rate and time semiautonomous mobile robot in an indoor environment efficiency Virdi et al (2017) Home automation control system implementation using SSVEP based Success rate and time efficiency brain-computer interface A hybrid EEG-based BCI for robot grasp controlling Success rate and time efficiency Won et al 2014A BCI speller based on SSVEP using high frequency stimuli design Subjective fatigue and success rate …”

Section: Bci Equipmentmentioning

confidence: 99%

“…Eng. Applied Sci.,15 (2): 659-678, 2020 Ruhunage et al (2017) EMG signal controlled transhumeral prosthetic with EEG-SSVEP based approach for hand 5 open/close Savic and Popovic (2015) Brain computer interface prototypes for upper limb rehabilitation: a review of principles and 6 experimental results Stawicki et al (2016) Driving a semiautonomous mobile robotic car controlled by an SSVEP-Based BCI 61 Su and Li (2017) brain-computer interface based stochastic navigation and control of a semiautonomous mobile 5 robot in an indoor environment Virdi et al (2017) Home Automation control system implementation using SSVEP based brain-computer interface 10 Zhang et al 2017A hybrid EEG-based BCI for robot grasp controlling 6 Won et al 2014A BCI speller based on SSVEP using high frequency stimuli design 4 Wu et al (2016) A new SSVEP based BCI application on the mobile robot in a maze game 10 Yang et al 2017Mind control of a robotic arm with visual fusion technology 2 Li et al (2013) A hybrid BCI system combining P300 and SSVEP and Its application to wheelchair control 8 2017Real time classification of SSVEP brain activity with Not specified* Not specified Not specified adaptive feedforward neural networks Turnip et al (2017) Design of extraction method of SSVEP brain activity Not specified Not specified Not specified with IIR chebyshev Achic et al (2016) Hybrid BCI system to operate an electric wheelchair Emotiv epoc Emotiv epoc Saline based and a robotic arm for navigation and manipulation tasks electrodes Andronicus et al…”

Section: Bci Equipmentmentioning

confidence: 99%

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Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

Zavala¹,

López²,

Chicaiza³

et al. 2019

J. Eng. Appl. Sci.

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Brain-computer interface is a technology which creates a new way of communication between a person's brain and the external world. To achieve this objective, the brainwaves of a person must be gathered by using specialized devices and then classified in different categories that are associated with specific commands. In the process of brainwave gathering, brain activities of a person can be influenced by different types of stimuli to get the desired results and one of the most important and popular stimuli used in this field is steady state visually evoked potential. Based on this background, this review seeks to show and analyze a series of articles that have been executed around the world related to brain-computer interface applications using steady state visually evoked potential. This review has been executed with the objective of identifying the advantages and limitations of utilizing steady state visually evoked potentials, its main areas of application and the future challenges. Additionally, this review analyzes the different technologies involved to the implementation of state visually evoked potential systems such as signal classification techniques, electroencephalography devices, channels, verification metrics and experimental environments used in the research projects. In summary, this review intends to guide the scientific community about the different aspects involved in conducting research on the development of brain-computer interface applications using electroencephalography devices and steady state visually evoked potential.

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Section: Unmanned Ground Vehiclesmentioning

confidence: 99%

Section: Analysis Of Technical Specifications Of Previous Work Methomentioning

confidence: 99%

Section: Bci Equipmentmentioning

confidence: 99%

Section: Bci Equipmentmentioning

confidence: 99%

See 2 more Smart Citations

Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

Zavala¹,

López²,

Chicaiza³

et al. 2019

J. Eng. Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…When the subject looks at a given command on the screen, the associated stimulus frequency is detected in the EEG signal and the command is triggered. Stawicki et al [48] follow the same approach, using a screen, but illustrate the commands in an interface based on the subjective view of the robot, generated by a camera located on the mobile robot itself. A slightly more sophisticated approach consists of introducing an avatar to represent the possible actions [13].…”

Section: State Of the Artmentioning

confidence: 99%

Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

Luca¹,

Karim

Mondada

2016

Swarm Intell

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Search and rescue, autonomous construction, and many other semi-autonomous multi-robot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection. We use electroencephalography (EEG) signals to select the robot at which the operator is looking. This is achieved using steady-state visually evoked potential (SSVEP), a repeatable neural response to a regularly blinking visual stimulus that varies predictively based on the blinking frequency. In our experiments, each robot was equipped with LEDs blinking at a different frequency, and the operator's SSVEP neural response was extracted from the EEG signal to detect and select the robot without requiring any conscious action by the user. This study systematically investigates several parameters affecting the SSVEP neural response: blinking frequency of the LED, distance between the robot and the operator, and color of the LED. Based on these parameters, we study two signal processing approaches and critically analyze their performance on 10 subjects controlling a set of physical robots. Our results show that despite numerous artifacts, it is possible to achieve a recognition rate higher than 85% on some subjects, while the average over the ten subjects was 75%.

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