Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

Qiu, Shiyuan; Li, Zhijun; He, Wei; Zhang, Longbin; Yang, Chenguang; Su, Chun‐Yi

doi:10.1109/tfuzz.2016.2566676

Cited by 89 publications

(40 citation statements)

References 44 publications

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“…The output of the spatial convolutional layer is the linear combination of all the 22 channels. Then the spatial feature maps are transmitted to the mean pooling layer with size (1,3) and stride (1,3). In the last classification layer, global convolution filters are applied to produce feature maps with size (1,1) and pass these feature maps to a softmax classifier directly, yielding the probability of the input belonging to each classes.…”

Section: B Deep Learning Modelmentioning

confidence: 99%

“…In healthcare domain, EEG signals are introduced to detect the organic brain injury or predict epileptic seizure [2]. Some BCI systems based on EEG have already allowing paralyzed patients to interact with wheelchairs or control a robot through their motor imagery EEG signals [3,4]. Motor imagery EEG is a kind of signals collected when a subject imagines performing a certain action (e.g., closing eyes or moving feet) but does not make an actual movement [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

Zhang

Lei

Yang

2018

2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)

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Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples. In order to solve the issue of data insufficiency in deep learning for EEG decoding, we propose a novel data augmentation method that add perturbations to amplitudes of EEG signals after transform them to frequency domain. In experiments, we explore the performance of signal recognition with the state-ofthe-art models before and after data augmentation on BCI Competition IV dataset 2a and our local dataset. The results show that our data augmentation technique can improve the accuracy of EEG recognition effectively.

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Section: B Deep Learning Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

Zhang

Lei

Yang

2018

2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)

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show abstract

“…With the developments of commercial available robotic arms: iARM 25 (produced by Exact Dynamics Ò ) and JACO 26 (produced by Kinova Ò ), a lot of researchers both at home and abroad carried out a series of studies, such as the FRIEND series robots 27 in the University of Bremen, the WMRA-I and II 28 in the University of South Florida, the wheelchair-mounted robotic manipulators (WMRMs) in Purdue University, 29 and the WMRA system ''WIM'' 3,4 in Waseda University. At present, the researches on the WMRA mainly focus on the following categories: (1) develop a variety of human-robot interaction interfaces that can satisfy the motor ability of different users, particularly for the disables; 1,4,[30][31][32] (2) adopt the visual servo technology or learn from demonstration to control the motion of robotic arm, so as to reduce the physical and mental burden of the users; 27,33,34 (3) design a new structure of robotic arm in order to improve the safety of the user, for the users are in the working space of the robotic arm; 35 and (4) develop the integrated WMRA with two or more above characteristics. To develop the WMRA with the characteristics of high safety, high intelligence, and operational ease is the developing trend in the field of assistive robots.…”

Section: Wmramentioning

confidence: 99%

Learning motion primitives from demonstration

Chi

Yao

Liu

et al. 2017

Advances in Mechanical Engineering

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With the purpose to offer simple and convenient assistance for the elders with disabilities to take care of themselves in activities of daily living, we present a motion primitives learning method based on robot learning from demonstration to improve the intelligence and adaptability of the wheelchair-mounted robotic arm. This method adopts the beta process autoregressive hidden Markov model to segment the demonstrations of related task, acquire the contained motion primitives, and recognize the repeated motion primitives. After that, it adopts the dynamic movement primitives to adjust the related motion primitives according to the task instructions by the wheelchair-mounted robotic arm users, so as to replay the demonstrated task in a new environment. This learning framework is validated on a 6-degree-of-freedom JACO robotic arm, performing the tasks of drinking water from the bottle through a straw and pouring water from the bottle to the cup.

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“…In [11], Fuzzy Logic Controller (FLC) was proposed for robot tracking and Proportional-Integral-Derivative (PID) controller was utilized to control the speed of WMRs in [12]. The authors of [13] presented an observer-based approach, whereas backstepping control strategy was presented in [14,15].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles

Bamgbose¹,

Li²

2019

Adv. sci. technol. eng. syst. j.

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For mission-critical and time-sensitive navigation of autonomous vehicles, controller design must exhibit excellent tracking performance with respect to the speed of convergence to reference command and steady-state accuracy. In this article, a novel design integration of the neural network with the traditional control system is proposed to adaptively obtain optimized controller parameters resulting in improved transient and steady-state performance of motion and position control of autonomous vehicles. Application of the proposed intelligent control scheme to mobile robot navigation was presented for an eightshaped trajectory by optimizing a Lyapunov-based nonlinear controller. Furthermore, a Linear Quadratic Regulator-based controller was optimized based on the proposed strategy to control the pitch and yaw angles of a 2-Degree-of -Freedom helicopter. The simulation results showed that the proposed scheme outperforms the traditional controllers in terms of the speed of convergence to the desired trajectory and overall error minimization.In order to execute maneuvers for unmanned Air Vehicles (UAVs), feedback linearization [24,25] and sliding mode control [26] have been proposed, but they have failed for certain models due to the nonlinear dynamics of the vehicle [27]. For helicopter control, backstepping control strategy [28] and Linear Quadratic Regulator (LQR)-based control [29] have been proposed.

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Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot

Cited by 89 publications

References 44 publications

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

Learning motion primitives from demonstration

Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles

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