EEG-controlled meal assistance robot with camera-based automatic mouth position tracking and mouth open detection

Perera, Chamika Janith; Lalitharatne, Thilina Dulantha; Kiguchi, Kazuo

doi:10.1109/icra.2017.7989208

Cited by 31 publications

(14 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. The system was created to help people with any type of movement limitations in their upper limbs.…”

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

“…The system processes the brain signals by means of spatial and high-pass filters and the frequency domain is extracted and classified using a simple threshold and Fourier transform. Similarly, the research Perera et al (2017) proposes a system for controlling a robotic food assistance arm. The system uses 6, 7 and 8 Hz to control an arm with 4 Degrees of Freedom (DOF).…”

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 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%

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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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