A prototype of SSVEP-based BCI for home appliances control

Anindya, Sinantya Feranti; Rachmat, Hendi Handian; Sutjiredjeki, Ediana

doi:10.1109/ibiomed.2016.7869810

Cited by 22 publications

(13 citation statements)

References 11 publications

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“…In such solution, different IoT devices such as lights, fans, televisions and alarms of a house are controlled by 10-13 Hz stimulus frequencies. Furthermore, a platform prototype for multiple devices control has been developed by Anindya et al (2016); the system allows to control up to three connected devices and it uses a low stimulus frequencies set (6, 6.5, 7, 7.5, 8.2, 9.3, 10, 12 Hz) and a high frequencies set (8, 14, 28 Hz).…”

Section: Internet Of Thingsmentioning

confidence: 99%

“…Similarly, OpenBCI and Emotiv EPOC also demonstrate to be a popular solution with 5 articles each. On the other hand, other articles such as Turnip et al (2015a-c); Andronicus et al (2015); Turnip et al (2016); Anindya et al (2016)and Turnip et al (2017)did not specify the BCI hardware that they used.…”

Section: Bci Equipmentmentioning

confidence: 99%

“…Additionally, we could observe that 19 of the articles used an EEG electrode cap; although, the articles did not detail the model, the information was inferred using the Turnip et al (2015a-c) EEG-SSVEP signals extraction with nonlinear adaptive filter for 3 O1, O2, Pz brain-controlled wheelchair Turnip et al (2016) Utilization of EEG-SSVEP method and ANFIS classifier for controlling electronic wheelchair 3 O1, O2, Pz Turnip et al (2017) Real time classification of SSVEP brain activity with adaptive feedforward neural networks 3 O1, O2, Oz Turnip et al (2017) Design of extraction method of SSVEP brain activity with IIR chebyshev 3 O1, O2, Oz Achic et al (2016) Hybrid BCI system to operate an electric wheelchair and a robotic Arm for navigation and manipulation Tasks 4 P7, O1, O2, P8 Andronicus et al (2015) Heuristic steady state visual evoked potential based brain computer interface system for robotic wheelchair application 1 Oz Anindya et al (2016) A prototype of SSVEP-based BCI for home appliances control 4 O1, O2, POz, Oz Bevilacqua et al (2014) A novel BCI-SSVEP based approach for control of walking in virtual environment using a convolutional neural network 6 Cz, Pz, PO3, PO4, Oz, AFz Zhang et al (2012) A simple platform of brain-controlled mobile robot and its implementation by SSVEP 4 Oz, O1, O2, Pz Diez et al (2014) Mobile robot navigation with a self-paced brain-computer interface based on high-frequency SSVEP 3 O1, Oz, O2 González-Mendoza Brain computer interface based on SSVEP for controlling a remote 2 Fz, Oz et al 2015control car Gui et al (2015) Online brain-computer interface controlling robotic exoskeleton for 4 O1, O2, PO3, PO4 gait rehabilitation Guneysu and Akin An SSVEP based BCI to control a humanoid robot by using portable 1 O1, O2 2013EEG device Zhao et al (2017) Behavior-based SSVEP hierarchical architecture for telepresence 9 Oz, POz, PO3, PO4, PO5, control of humanoid robot to achieve full-body movement PO6, Pz, P1, P2 Kang et al 2016Brain-machine interfacing-based teleoperation of multiple 24 SP1, SP2, FP1, FP2, F7, F3, coordinated mobile robots FZ, F4, F8, T3, C3, CZ, C4, T4, T5, P3, PZ, P4, T6, REF, O1, Oz, O2, GND Khan et al (2016) Hybrid EEG-NIRS based active command generation for quadcopter 14 F3, AF4, F3, F4, F7, F8, FC5, movement control FC6, T7, T8, P7, P8, O1, O2 Zhang et al (2011) A new object-oriented SSVEP-based BCI paradigm using 6 PO3, Pz, PO4, O1, Oz, O2 continuous action scene Lakmazaheri et al…”

Section: Bci Equipmentmentioning

confidence: 99%

“…Success rate system for robotic wheelchair application Anindya et al (2016) A prototype of SSVEP-based BCI for home appliances control Success rate Bevilacqua et al (2014) A novel BCI-SSVEP based approach for control of walking in virtual Success rate environment using a convolutional neural network Zhang et al (2012) A simple platform of brain-controlled mobile robot and its implementation Success rate and information by SSVEP transfer rate Diez et al (2014) Mobile robot navigation with a self-paced brain-computer interface Success rate and time efficiency based on high-frequency SSVEP Gonzalez-Mendoza et al Brain Computer Interface based on SSVEP for controlling a remote control car Success rate (2015) Gui et al (2015) Online brain-computer interface controlling robotic exoskeleton for gait Duration of the transitional state rehabilitation and rate of recognition of the steady state Guneysu and Akin (2013) An SSVEP based BCI to control a humanoid robot by using portable EEG device Success rate Zhao et al (2017) Behavior-based SSVEP hierarchical architecture for telepresence control of Success rate and time efficiency humanoid robot to achieve full-body movement Kang et al…”

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: Internet Of Thingsmentioning

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

“…Some of the applications of BCI are wheelchair control [3][4], unmanned aerial vehicle [5][6], home appliances [7] , robot [8] and vehicle control approaches [9][10]. One of the most important components of BCI is the feature extraction of EEG signals.…”

Section: Introductionmentioning

confidence: 99%