Brain computer interface based robotic arm control

Latif, Muhammad Yasir; Naeem, Laiba; Hafeez, Tehmina; Raheel, Aasim; Saeed, Sanay Muhammad Umar; Awais, Muhammad; Alnowami, Majdi R.; Anwar, Syed Muhammad

doi:10.1109/isc2.2017.8090870

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…Moreover, seven articles used Biosemi equipment [ 236 , 237 , 238 , 239 , 240 , 241 , 242 ], and four articles used Medical Computer Systems equipment [ 243 , 244 , 245 , 246 ]. Three articles used Medicom MTD equipment [ 247 , 248 , 249 ], and another three articles used the equipment from InteraXon company [ 250 , 251 , 252 ]. Two articles each used Advanced Brain Monitoring [ 253 , 254 ] and Nihon Kohden [ 255 , 256 ] equipment.…”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

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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“…There are two types of non-invasive electrodes, wet and dry, the former requiring soaking in a saline solution before use (Bularka & Gontean, 2016;Latif et al, 2017). Wet consumer-grade EEG devices, such as the Emotive Epoc, usually take more than 15 minutes to set up.…”

Section: Brain-computer Interfacesmentioning

confidence: 99%

Mind Games: A Web-Based Multiplayer Brain-Computer Interface Game

Louis

Alikhademi

Joseph

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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The advancement of consumer-grade Brain-Computer Interface (BCI) devices has led to the use of brain wave data in disciplines beyond neuroscience. While BCI devices are studied holistically in some domains, the potential remains unexplored in other applicable domains, such as gaming. The majority of BCI games are single-player, only work on a single platform, or require expensive hardware that is difficult to use. To address these challenges, we propose Mind Games: a competitive multiplayer tug-of-war game played entirely with brain wave data in a web browser with a low-cost consumer-grade BCI device. Unlike other BCI games, Mind Games is platform-independent, has a quick setup time, and uses the latest web-based technologies: web-Bluetooth and JavaScript frameworks for networking and signal processing. This new cross-platform BCI pipeline enables physiological computing applications to be more accessible, leading the future of online multiplayer BCI games.

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“…Electroencephalography (EEG) signal has been used purposely for communication between the human brain and a computer system, to diagnose epilepsy [76] and other brain disorders [77]. Currently, this signal capability is extended to establish communication between the human brain and a robotic system [78], and to send a control signal from a human to a robotic system [79].…”

Section: Sensors and Actuatorsmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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This paper presents a state-of-the-art survey on robotic systems, sensors, actuators and collaborative strategies for Physical Human-Robot Collaboration (pHRC). The paper starts with an overview of some robotic systems with cuttingedge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances made, some research directions, and future challenges are presented.

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Brain computer interface based robotic arm control

Cited by 17 publications

References 10 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

Mind Games: A Web-Based Multiplayer Brain-Computer Interface Game

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

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