SVM-based Brain–Machine Interface for controlling a robot arm through four mental tasks

Hortal, Enrique; Planelles, Daniel; Costa, Álvaro; Íáñez, Eduardo; Úbeda, Andrés; Azorín, José M.; Fernández, Eduardo

doi:10.1016/j.neucom.2014.09.078

Cited by 116 publications

(50 citation statements)

References 22 publications

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“…While we used motor imagery paradigm and decoded the subject’s intention under the ERD/ERS framework as we used in our previous study to control a quadcopter15, the present study represents an entirely new investigation for human subjects to control a robotic arm for reaching, grasping and moving using noninvasive EEG signals. Recent work18 has explored the combination of motor imagination and other cognitive activities like alphabetical or numerical exercises to drive a robotic arm to complete reach task in a plane. Our work extends and explores the full possibility of reach and grasp of objects in a three-dimensional space, and furthermore more complex tasks close to the activities of daily living (ADL) like moving an object from table onto the shelf was designed and examined in multiple sessions.…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, few research groups have attempted control of a prosthetic or a robotic arm using scalp EEG based BCIs. A variety of control signals, including sensorimotor rhythms18, steady state visual evoked potentials1920, hybrid systems21, real movement or attempted movement2223, have been used for these initial studies to control the robotic or prosthetic arm. Such previous efforts have primarily constrained the BCI control system to be discrete in one dimension or a plane without exploring the full possibility of controls in three-dimensional space.…”

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confidence: 99%

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Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Meng

Zhang

Bekyo

et al. 2016

Sci Rep

399

292

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Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Meng

Zhang

Bekyo

et al. 2016

Sci Rep

399

292

View full text Add to dashboard Cite

show abstract

“…[3][4] resented a study on human identification using peak matching of VEP signal and classified standard distance measure based algorithm. [10][11][12][13][14] have been proposed a ECG signal for person identification [15][16] extracted features from EEG Phase space and optimal kernel based.…”

Section: Background Studymentioning

confidence: 99%

Information-Theoretic Measures on Intrinsic Mode Function for the Individual Identification Using EEG Sensors

Kumari

Vaish

2015

IEEE Sensors J.

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In spite of recent advances, the interest in extracting knowledge hidden in the electroencephalogram(EEG) signals is rapidly growing, as well as their application in the computational neuro engineering field such as mobile robot control, wheelchair control and person identification using brainwaves. The large number of methods for EEG feature extraction demands a good feature for every task. Digging up the most unique feature would be worth for identification of individual using EEG signal. This research presents a novel approach for feature extraction of electroencephalogram (EEG) signal using the Empirical mode decomposition (EMD) and information-theoretic-method (ITM). The EMD technique is applied to decompose an EEG signal into set of intrinsic mode function (IMF). These decomposed signals are of the same length and in the same time domain as the Original Signal. Hence, EMD method preserves varying frequencies in time. To measure the performance of the features, we have used Hybrid learning for classification where we have selected Learning Vector Quantization Neural Network (LVQ-NN) with fuzzy algorithm. Furthermore, to investigate the performance and accuracy of each subject over the different cognitive tasks based on Cohen's kappa coefficient. The results are compared with past methods in literature for feature extraction and classification methods. Results confirm that proposed features present a satisfactory performance.

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“…BCIs are primary researched as a mean of terminal input devices [48], and control of powered wheelchairs [49], assistive robots [50] and smart home [51].…”

Section: Neuroelectric Signal-based Interfacesmentioning

confidence: 99%

Control systems of rehabilitation engineering equipment — A review

Ianoşi-Andreeva-Dimitrova

Mândru

2015

2015 E-Health and Bioengineering Conference (EHB)

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This article aims to review the state of the art of control systems that interface individuals with motor and sensorial disabilities and their rehabilitation equipment. It begins with the simplest form of body-powered interfaces and continues with the most recent tendencies. A rehabilitation equipment control system taxonomy is presented, as well as its associated human-body topography. (Abstract) Keywords: Assistive technology, Man machine systems, Braincomputer interfacesI.

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SVM-based Brain–Machine Interface for controlling a robot arm through four mental tasks

Cited by 116 publications

References 22 publications

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Information-Theoretic Measures on Intrinsic Mode Function for the Individual Identification Using EEG Sensors

Control systems of rehabilitation engineering equipment — A review

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