The Future of Work: Towards Service Robot Control through Brain-Computer Interface

Georgescu, Leonardo; Wallace, Dylan; Kyong, Daniel; Chun, Alex; Chun, K . W .; Oh, Paul

doi:10.1109/ccwc47524.2020.9031211

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…In this scenario, BCI must be sufficiently advanced to allow a reliable transmission of the "thought" braking command to the mobile device. It should also be faster than our peripheral nervous transmission to become valuable regarding accidents prevention, which is far from being the case at present (e.g., Royer et al, 2010;Kim and Lee, 2017;Georgescu et al, 2020).…”

Section: Device's Control: Reactive and Active Bcimentioning

confidence: 99%

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Douibi

Bars

Lemontey

et al. 2021

Front. Hum. Neurosci.

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In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

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Section: Device's Control: Reactive and Active Bcimentioning

confidence: 99%

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Douibi

Bars

Lemontey

et al. 2021

Front. Hum. Neurosci.

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show abstract

“…These included leveraging a one-dimensional convolutional neural network (CNN) [3,4], and more advanced architectural designs, such as multi-layer CNNs [5] or a deep residual CNN [6,7], which has exhibited significant success in these applications. Moreover, numerous research papers have investigated the integration of deep learning networks with steering interfaces, aiming to establish systems capable of translating users' brain activity into movement instructions for vehicles, such as a hexapod [8], a telepresence robot control interface based on a support vector machine (SVM) [9,10], wheelchair control based on motor imagery and fuzzy logic [11], multi-scale CNNs [12], multilevel weighted feature fusion [13], and power spectrum estimation [14]. Despite the great progress in the field of interpreting human thoughts, the control over the vehicle is often limited to a single direction [8,15].…”

Section: Introductionmentioning

confidence: 99%

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery

Kocejko,

Matuszkiewicz,

Durawa

et al. 2024

Sensors

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This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10–20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

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“…In their research, George et al [27] present a BCI headset controlling a service robot's navigation using a SVM classifier while an operator performs motor imagery tasks.…”

Section: Introductionmentioning

confidence: 99%

Biologically-Inspired Legged Robot Locomotion Controlled With a BCI by Means of Cognitive Monitoring

et al. 2021

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Brain-computer interfaces(BCI) are a mechanism to record the electrical signals of the brain and translate them into commands to operate an output device like a robotic system. This article presents the development of a real-time locomotion system of a hexapod robot with bio-inspired movement dynamics inspired in the stick insect and tele-operated by cognitive activities of motor imagination. Brain signals are acquired using only four electrodes from a BCI device and sent to computer equipment for processing and classification by the iQSA method based on quaternion algebra. A structure consisting of three main stages are proposed: (1) signal acquisition, (2) data analysis and processing by the iQSA method, and (3) bio-inspired locomotion system using a Spiking Neural Network (SNN) with twelve neurons. An off-line training stage was carried out with data from 120 users to create the necessary decision rules for the iQSA method, obtaining an average performance of 97.72%. Finally, the experiment was implemented in realtime to evaluate the performance of the entire system. The recognition rate to achieve the corresponding gait pattern is greater than 90% for BCI, and the time delay is approximately from 1 to 1.5 seconds. The results show that all the subjects could generate their desired mental activities, and the robotic system could replicate the gait pattern in line with a slight delay. INDEX TERMS Bio-inspired robot, Brain-Computer Interface (BCI), electroencephalography, hexapod robot, iQSA method, motor imagery, spiking neural network, central pattern generator.

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The Future of Work: Towards Service Robot Control through Brain-Computer Interface

Cited by 7 publications

References 9 publications

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery

Biologically-Inspired Legged Robot Locomotion Controlled With a BCI by Means of Cognitive Monitoring

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