A new SSVEP based BCI application on the mobile robot in a maze game

Wu, Chung-Min; Chen, Yeou-Jiunn; Zaeni, Ilham Ari Elbaith; Chen, Shih-Chung

doi:10.1109/icamse.2016.7840197

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…The system captures the signals using a bioamplifier, then converts the analog signals to digital ones and finally they are processed to control the RC car. Similarly, Wu et al (2016) shows a BCI system that controls an RC car inside a labyrinth; one of the objectives of the research is to get a system that avoids obstacles. The system uses a NuAmp EEG amplifier and a remote control car that uses four types of signals i.e., 7, 8-10 Hz for its movements i.e.…”

Section: Unmanned Ground Vehiclesmentioning

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%

“…Heuristic steady state visual evoked potential based brain 2015computer interface system for robotic wheelchair application Not specified* Not specified Not specified Anindya et al 2016 Liu et al (2018) Design of a video feedback SSVEP-BCI system for car control based on improved MUSIC method GES 300 system 64-channel Not specified adult-sized head cap Turnip et al (2016) An application of online ANFIS classifier for wheelchair based Not specified EEG electrode cap* Not specified brain computer interface Won et al (2014) A BCI Speller based on SSVEP using high frequency BrainAmp EEG electrode cap* Not specified stimuli design Wu et al (2016) A new SSVEP based BCI application on the mobile robot NuAmp EEG Not specified Not specified in a maze game amplifier Yang et al (2017) Mind control of a robotic arm with visual fusion technology Neuroscan EEG electrode Not specified amplifier cap Li et al (2013) A Hybrid BCI system combining P300 and SSVEP and NuAmp EEG EEG electrode Ag-AgCl its application to wheelchair control amplifier cap (LT37) electrodes *Not mentioned by the researchers. Deduced using the figures presented in the article figures provided in the articles.…”

Section: Bci Equipmentmentioning

confidence: 99%

See 3 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.

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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: Bci Equipmentmentioning

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

“…Among the different ways of decoding brain activity, Electroencephalography (EEG) is receiving a strong interest by the scientific community since it is non-invasive, cheap, and is endowed with high temporal resolution to allow real-time operation [3], [4]. In fact, different EEG-based BCI paradigms, such as P300 [5] and Motor Imagery [6]- [10] have already been successfully employed in several contexts but, in particular, Steady-State Visually Evoked Potentials (SSVEPs) have gained outstanding relevance for the development of applications in healthcare, [11], [12] entertainment [13], and industry [14], [15] owing to quick response, easy detection, high signalto-noise ratio (SNR) [16]. As a matter of fact, the classification of SSVEPs can be performed with good results even with simple, trainingless algorithms, such as Power Spectral Density Analysis (PSDA) or Canonical Correlation Analysis (CCA) [17].…”

Section: Introductionmentioning

confidence: 99%

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

et al. 2023

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This work addresses the employment of Machine Learning (ML) and Domain Adaptation (DA) in the framework of Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs). Currently, all the state-of-the-art classification strategies do not consider the high non-stationarity typical of brain signals. This can lead to poor performance, expecially when short-time signals have to be considered to allow real-time human-environment interaction. In this regard, ML and DA techniques can represent a suitable strategy to enhance the performance of SSVEPs classification pipelines. In particular, the employment of a two-step DA technique is proposed: first, the standardization of the data per subject is performed by exploiting a part of unlabeled test data during the training stage; second, a similarity measure between subjects is considered in the selection of the validation sets. The proposal was applied to three classifiers to verify the statistical significance of the improvements over the standard approaches. These classifiers were validated and comparatively tested on a well-known public benchmark dataset. An appropriate validation method was used in order to simulate real-world usage. The experimental results show that the proposed approach significantly improves the classification accuracy of SSVEPs. In fact, up to 62.27 % accuracy was achieved also in the case of short-time signals (i.e., 1.0 s). This represents a further confirmation of the suitability of advanced ML to improve the performance of BCIs for daily-life applications.

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