SSVEP based BMI for a meal assistance robot

Perera, Chamika Janith; Naotunna, Isira; Sadaruwan, Chameera; Gopura, R. A. R. C.; Lalitharatne, Thilina Dulantha

doi:10.1109/smc.2016.7844580

Cited by 34 publications

(22 citation statements)

References 9 publications

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“…Based on this background, this review has analyzed the most recent research articles related to EEG-SSVEP implementations intended to be used in real life. The reviewed articles were classified in different categories as shown in Peng et al (2016), an assistant arm for patient feeding Perera et al (2016Perera et al ( , 2017, the control of a transhumeral prosthesis Ruhunage et al (2017) and robotic arms Yang et al (2017); Zhang et al (2017a, b); Pelayo et al (2018); Zhang et al (2012). Among the reviewed articles, Peng et al (2016) proposes an electric hospital bed controlled by SSVEP-P300 combination responses.…”

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

“…Within the robotic arm control implementations we have Perera et al (2017Perera et al ( , 2016; Ruhunage et al (2017a, b); Yang et al (2017); ; Pelayo et al (2018); Zhang et al (2018), several solutions are presented. According to Perera et al (2017), the researchers developed a BCI system to control a robotic arm that aids in people's feeding process.…”

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

“…Applicability of SSVEP-based brain-computer interfaces 4 O1, O2, Oz, POz for robot navigation in real environments Lee et al (2012) A brain-wave-actuated small robot car using ensemble empirical mode decomposition-based approach 1 Oz Li et al (2018) A human-vehicle collaborative simulated driving system based 13 T7, T8, P7, P8, FC3, FC4, on hybrid brain-computer interfaces and computer vision C3, C4, CP3, CP4, P3, P4, Oz Liu et al (2018) Design of a video feedback SSVEP-BCI system for car control based on improved MUSIC method 3 O1, Oz, O2 Turnip et al (2016) An application of online ANFIS classifier for wheelchair based brain computer interface 3 O1, O2, Pz Wang et al (2018) A Wearable SSVEP-Based BCI System for quadcopter Control Using Head-Mounted Device 6 PO3, PO4, O1, O2, C5, C6 Mistry et al (2018) An SSVEP based brain computer interface system to control electric wheelchairs 6 P3, Pz, P4, O1, Oz, O2 Nakanishi et al (2017) Enhancing detection of SSVEPs for a high-speed brain 10 Pz, PO5, PO3, POz, PO4, speller using task-related component analysis PO6, O1, Oz, O2, Cz Pelayo et al (2018) Brain-Computer Interface Controlled Robotic Arm to Improve 6 Not specified Quality of Life Peng et al (2016) Control of a nursing bed based on a hybrid brain-computer interface 8 P7, P3, Pz, P4, P8, O1, Oz, O2 Perera et al (2016) EEG-controlled meal assistance robot with camera-based automatic 8 O1, O2, POz, PO3, PO4, mouth position tracking and mouth open detection C1, C2, C3 Perera et al (2017) SSVEP Based BMI for a meal assistance robot 8 O1, O2, POz, PO3, PO4, C1, C2, C3 Zhao et al (2017) A SSVEP intelligent home service system based on CCA Not specified Not specified Ruhunage et al (2017) EMG signal controlled transhumeral prosthetic with 2 POz, Oz EEG-SSVEP based approach for hand open/close Savic and Popovic (2015) Brain computer interface prototypes for upper limb 3 C3, Oz, Cz Rehabilitation: a review of principles and experimental results 2017Brain-computer interface based stochastic navigation and control 4 PZ, O1, Oz, O2 of a semiautonomous mobile robot in an indoor environment Virdi et al (2017) Home automation control system implementation using 8 PO7, PO3, PO4, POz, PO8, SSVEP based brain-computer interface O1, O2, Oz Zhang et al (2016) A hybrid EEG-based BCI for robot grasp controlling 8 P7, P3, Pz, P4, P8, O1, Oz, O2 Won et al 2014A BCI Speller based on SSVEP using high frequency stimuli design 15 P5, P3, P1, Pz, P2, P4, P6, PO7, PO3, POz, PO4, PO8, O1, Oz, O2 Wu et al 2016A new SSVEP based BCI application on the mobile robot in a maze 4 O1, Oz, O2, Pz game Yang et al (2017) Mind control of a robotic arm with vis...…”

Section: Bci Equipmentmentioning

confidence: 99%

“…Applicability of SSVEP-based brain-computer interfaces for robot navigation in real environments 15 Lee et al (2012) A brain-wave-actuated small robot car using ensemble empirical mode 11 decomposition-based approach Li et al (2013) A human-vehicle collaborative simulated driving system based on hybrid brain-computer 6 interfaces and computer vision Liu et al (2018) Design of a video feedback SSVEP-BCI system for car control based on improved MUSIC method Not specified Turnip et al (2016) An application of online ANFIS classifier for wheelchair based brain computer interface 4 Wang et al (2018) A wearable SSVEP-Based BCI system for quadcopter control using head-mounted device 5 Mistry et al (2018) An SSVEP based brain computer interface system to control electric wheelchairs 4 Nakanishi et al (2017) Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis 12 and 20 Pelayo et al (2018) Brain-Computer Interface Controlled Robotic Arm to improve quality of Life 3 Peng et al (2016) Control of a nursing bed based on a hybrid brain-computer interface 8 Perera et al (2016) EEG-controlled meal assistance robot with camera-based automatic mouth position tracking and 6 mouth open detection Perera et al (2017) SSVEP Based BMI for a meal assistance robot 5 Zhao et al (2017) A SSVEP intelligent home service system based on CCA 10 J. Eng.…”

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: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

Section: Review Of Eeg-ssvep Applicationmentioning

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

“…Different EEG paradigm-based BMIs have been employed to control the dexterous robotic arm. Since sufficient number of discrete user commands could be inferred with the steady-state visual evoked potential (SSVEP)-based BMI or P300-based BMI in theory, a lot of studies have utilized such BMIs to control the robotic arm (Perera et al, 2016;Hong and Khan, 2017;Qiu et al, 2017;Zhang et al, 2017;José de Jesús, 2018;Kumar et al, 2018;Chen et al, 2019;Duan et al, 2019;José de Jesús et al, 2019a,b). However, they involve flickering displays which may make some BMI user uncomfortable (Graimann et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Zeng

Shen

et al. 2020

Front. Neurorobot.

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Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.

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Linear Diophantine equation (LDE) decoder: A training‐free decoding algorithm for multifrequency SSVEP with reduced computation cost

Tan

Grayden

et al. 2023

Asian Journal of Control

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Multifrequency steady‐state visual evoked potentials (SSVEPs) have been developed to extend the capability of SSVEP‐based brain‐machine interfaces (BMIs) to complex applications that have large numbers of targets. Even though various multifrequency stimulation methods have been introduced, the decoding algorithms for multifrequency SSVEP are still in early development. The recently developed multifrequency canonical correlation analysis (MFCCA) was shown to be a feasible training‐free option to use in decoding multifrequency SSVEPs. However, the time complexity of MFCCA is shown to be , which will lead to long computation time as grows, where represents the input size in decoding. In this paper, a novel decoding algorithm is proposed with the aim to reduce the time complexity. This algorithm is based on linear Diophantine equation solvers and has a reduced computation cost while remaining training‐free. Our simulation results demonstrated that linear Diophantine equation (LDE) decoder run time is only one fifth of MFCCA run time under respective optimal settings on 5‐s single‐channel data. This reduced computation cost makes it easier to implement multifrequency SSVEP in real‐time systems. The effectiveness of this new decoding algorithm is validated with nine healthy participants when using dry electrode scalp electroencephalography (EEG).

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SSVEP based BMI for a meal assistance robot

Cited by 34 publications

References 9 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

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Linear Diophantine equation (LDE) decoder: A training‐free decoding algorithm for multifrequency SSVEP with reduced computation cost

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