Heuristic Steady State Visual Evoked Potential based Brain Computer Interface system for robotic wheelchair application

Andronicus, Stephen; Harjanto, Nathaniel Chandra; Suprijanto,; Widyotriatmo, Augie

doi:10.1109/icici-bme.2015.7401342

Cited by 2 publications

(6 citation statements)

References 3 publications

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“…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%

“…Navigation assistive technologies: Navigation assistive technologies are created to provide support to people with disabilities in order to help them be more independent with their mobility. One type of solution of this technology is the user controlled wheelchair presented by Li et al (2013); Turnip et al (2015a-c); Andronicus et al (2015); Turnip et al (2016); Turnip et al (2017Turnip et al ( , 2017; Turnip and Mistry et al (2018). There are several techniques that can be used for delivering user control to wheelchairs but SSVEP is one of the most popular because of its accuracy and usability.…”

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

“…Although, many of the articles detail the frequencies they use Andronicus et al (2015) and Turnip et al (2016) do not. Andronicus et al (2015) focuses on explaining the construction of a system with the minimum amount of electrodes to make more comfortable to use. This system consists of 3 parts: a pre-processing stage that uses a "centering process" to eliminate data offset, a feature extraction stage based on 4 bandpass filters with "14-order Chebyshev Type 2 IIR" and Power Spectral Density (PSD) estimation using the Welch method and a classification stage based on a "threshold and voting" system for classification into 4 patterns: forward, stop, left, right.…”

Section: Review Of Eeg-ssvep Applicationmentioning

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%

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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: Bci Equipmentmentioning

confidence: 99%

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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EEG Control of a Robotic Wheelchair

Chaudhary¹,

Gupta²,

Kumar³

et al. 2023

Human-Robot Interaction - Perspectives and Applications

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The Brain-Computer Interface (BCI) technology has been widely used in clinical research; however, its adoption in consumer devices has been hindered by high costs, poor reliability and limited autonomy. In this study, we introduce a low-cost, open-source hardware-based, consumer-grade product that brings BCI technologies closer to the elderly and motor-impaired individuals. Specifically, we developed an autonomous motorized wheelchair with BCI-based input capabilities. The system employs the ROS-backend navigation stack, which integrates RTAB-MAP for mapping, localization, and visual odometry, as well as A* global and DWA local path planning algorithms for seamless indoor autonomous operations. Data acquisition is accomplished using OpenBCI 16-channel EEG sensors, while Ensemble-Subspace KNN machine learning model is utilized for intent prediction, particularly goal selection. The system offers active obstacle avoidance and mapping in all environments, while a hybrid BCI Motor Imagery based control is implemented in a known mapped environment. This prototype offers remarkable autonomy while ensuring user safety and granting unparalleled independent mobility to the motor-impaired and elderly.

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Heuristic Steady State Visual Evoked Potential based Brain Computer Interface system for robotic wheelchair application

Cited by 2 publications

References 3 publications

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

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

EEG Control of a Robotic Wheelchair

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