Brain-Computer Interface Controlled Robotic Arm to Improve Quality of Life

Pelayo, Pablo; Murthy, Hemamalini; George, Kiran

doi:10.1109/ichi.2018.00072

Cited by 12 publications

(15 citation statements)

References 0 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%

“…In the same way, Pelayo et al (2018) presents a system that allows users to control a robotic arm through EEG signals based on SSVEP responses. The operation of the system is based on controlling 3 servomotors (servo 1: change the orientation, servo 2: change the angle, servo 3: change the claw's opening) using signals generated by the brain of the user when he/she looks at 3 LED lights of different frequencies (servo 1: 7 Hz, servo 2: 11 Hz, servo 3: 15 Hz).…”

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

“…As shown in Table 2 among 43 articles, 27 of them used the Oz channel while 30 used O1 or 2 as a solitary channel or in combination with other channels. It should also be noted that 31 of the articles used channels different from those indicated above; we assume that this was due to: Within the reviewed articles, 3 did not specify the used channels as seen in Yang et al (2017); Zhao et al (2016a, b) and Pelayo et al (2018) specified the number of used channels.…”

Section: Analysis Of Technical Specifications Of Previous Work Methomentioning

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%

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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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