A Hybrid Computer Interface for Robot Arm Control

Tang, Jingsheng; Zhou, Zongtan; Yu, Yang

doi:10.1109/itme.2016.0088

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…g.Tec equipment was used in 54 articles for the EEG method , 33 articles used Emotiv equipment , and 24 articles used Compumedics Neuroscan equipment . Further, 16 articles used Brain Products equipment [191][192][193][194][195][196][197][198][199][200][201][202][203][204][205][206], 13 articles used NeuroSky equipment [207][208][209][210][211][212][213][214][215][216][217][218][219], and Neuroelectrics [220][221][222][223][224][225][226][227] and OpenBCI [228][229][230][231][232][233][234][235] equipment were used in eight articles each. Moreover, seven articles used Biosemi equipment [236][237]…”

Section: Rq1: What Are the Publication Trends Based On Eeg Equipment?mentioning

confidence: 99%

“…Furthermore, only the wireless equipment from Emotiv company covered all four research areas, even though the sector of Emotiv mostly offers entertainment options and performance (http://bnci-horizon-2020.eu/ images/bncih2020/FBNCI_Roadmap.pdf, accessed on 25 February 2021). [194,199,205] actiHamp MD [192,198,201,203] BrainAmp EG [57], ET [78], MD [195][196][197]200 Overall, 17 companies provided more than two brands/products with different characteristics. For example, the company Neuroelectrics offers Enobio 8 and Enobia 32, which have up to 8 and 32 channels for EEG recording, respectively (https://www.neuroelectrics.…”

Section: Rq2: What Are the Most Common Types (Wired Or Wireless) Of Noninvasive Eeg-based Bci Equipment That Have Been Used In Brain Studmentioning

confidence: 99%

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Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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Section: Rq1: What Are the Publication Trends Based On Eeg Equipment?mentioning

confidence: 99%

Section: Rq2: What Are the Most Common Types (Wired Or Wireless) Of Noninvasive Eeg-based Bci Equipment That Have Been Used In Brain Studmentioning

confidence: 99%

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Belda-Lois et al (2011) recommend that future research should analyse and optimise the impact of rehabilitation based on signals from the cerebral cortex using functional near infrared spectroscopy (fNIRS). Brain-machine Interfaces (BMI) are becoming a recent trend in the field of rehabilitation robotics (Khan and Hong, 2017;Tang, Zhou and Yu, 2017). Miao et al (2018) argue that when the cortex is responsive to peripheral input, physiological signal control is an effective way of avoiding slacking and providing robotic support.…”

Section: Can Robots Bring Your Life Back? the Controversial And Recommendationsmentioning

confidence: 99%

Can a Robot Bring Your Life Back? A Systematic Review for Robotics in Rehabilitation

Chew

Turner

2019

Robotics in Healthcare

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Stroke is a leading cause of disability in the world and the use of robots in rehabilitation has become increasingly common. The Fourth Industrial Revolutions has created a novel and wide range of options for the involvement of computer-guided and artificially intelligent machines to be used in rehabilitation. In this chapter we critically review some of the literature on the use of robots in rehabilitation, and emphasize the diversity of approaches in this burgeoning field. We argue that there is a need to consolidate interdisciplinary evidence on robotics and rehabilitation in a systematic way, as the alternative is to have a literature that continues to grow, following the interests of various specialists, but without offering a synoptic assessment of what is available to medical specialists and patients. A literature review using Scopus and Web of Science, coupled with the Briggs Institute's Critical Appraisal Tool: Checklist for Case Reports was conducted. The two databases were systematically searched using inter-disciplinary keywords in Feb 2019. An initial search of the databases produced 9,894 articles. After rigorous reviews, 35 articles were screened and selected for further interpretation. We examined the current studies on the efficiency and effectiveness of the robot interventions and produced a taxonomy of the review. An original finding of the current robotics in rehabilitation landscaping are critical presented with recommendations and concluding remarks concerning interdisciplinary impact.

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“…Tomasz Kocejko [15] proposed a control system combining EMG and EEG and added the detection and analysis data of the eye tracker to realize the movement detection and analysis of prosthetic limbs. Jingsheng Tang et al [16] proposed a robotic arm control method combining EMG and EEG, using leg EMG signal to quickly and reliably select the currently activated joint, and using motor imagery signal to accurately control the computer interface. Agata Manolova et al [17] proposed a multimodal fusion method based on user electromyography, which achieved more reliable control on the precondition of reducing fatigue and inattention during use.…”

Section: Introductionmentioning

confidence: 99%

Robotic Arm Control System Based on Brain-Muscle Mixed Signals

Cheng

Yu³

et al. 2021

Preprint

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Aiming at the existing problems of BCI (brain computer interface), such as single input signal source, low accuracy of feature recognition, and less output control instructions, this paper proposes a robotic arm control system based on EEG (electroencephalogram) and EMG (electromyogram) mixed signals. The system flow is as follows: Firstly, the EMG signal of the unilateral arm and the EEG signal of the left and right hand motor imagery is collected synchronously. Then the collected EEG and EMG signals are extracted and classified, and the corresponding classification instructions are obtained. Finally, the multi-instruction real-time control of the robotic arm is realized under the classification instruction. The experimental verification results show that: The 10 subjects all realized the real-time multi-command control of the robotic arm, and the average recognition accuracy of each action reached more than 94%. The proposed system enriches the diversity of hybrid BCI and provides a theoretical basis and application foundation for the extended application of BCI in robotic arm control.

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A Hybrid Computer Interface for Robot Arm Control

Cited by 13 publications

References 6 publications

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Can a Robot Bring Your Life Back? A Systematic Review for Robotics in Rehabilitation

Robotic Arm Control System Based on Brain-Muscle Mixed Signals

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