A noninvasive brain–computer interface approach for predicting motion intention of activities of daily living tasks for an upper-limb wearable robot

Bandara, D. S. V.; Arata, Jumpei; Kiguchi, Kazuo

doi:10.1177/1729881418767310

Cited by 19 publications

(11 citation statements)

References 30 publications

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“…Next, the development of brain simulation software is 14.3% [ 49 , 50 , 51 ], and the development of hybrid architectures (using brain computing interfaces supported by neuromorphic processors) accounts for 14.3% [ 52 , 53 , 54 ]. The development and improvement of brain computing interfaces was 9.5% [ 55 , 56 ], as was analysis and database storage through machine learning [ 57 , 58 ]. Finally, 4.8% was shown for the design of neuromorphic processors [ 59 ].…”

Section: Methods and Resultsmentioning

confidence: 99%

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Hernández

Barbosa-Santillán

2022

Brain Sciences

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The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain–computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.

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Section: Methods and Resultsmentioning

confidence: 99%

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Hernández

Barbosa-Santillán

2022

Brain Sciences

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“…Time series input data were also applied with damping neurons in the adaptive neuron network controller for force control of robotic manipulators in unknown environments to consider the velocity and acceleration terms and effectively compensate for the unknown dynamics of the environment [42]. In addition, a time-delay feature matrix is used to provide inputs for neural networks and support vector machine-based classifiers, which collect the dynamic characteristics of EEG signals for motion intention prediction in [43]. Furthermore, the authors in [44] confirmed that the convolutive architecture with dynamic information as input is more accurate and robust than recurrent networks in longitudinal vehicle dynamics modeling.…”

Section: B Comparison Resultsmentioning

confidence: 99%

Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network

Xia

Kiguchi

2021

IEEE Access

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Robotic-assisted microsurgeries provide several benefits to both patients and surgeons. Nevertheless, there are still some limitations and challenges associated with their outcome, one of which is a lack of force feedback. Without force information, the risk of delicate tissue damage from the excessive force applied by surgeons would be increased. Since it is difficult to install force sensors on microsurgical tools, a novel approach for estimating a force vector from the deformation of the surgical tool is proposed in this paper. In the proposed approach, a surgical instrument that deforms according to the magnitude of the toolto-tissue force is designed, and a time series convolution neural network is used to make the nonlinear relationship between the visual information of the deformation of the surgical tool and the applied forces in such a way that the tool-to-tissue force can be estimated according to the deformation of the surgical instrument in a real-time manner. The experimental results prove that the applied force can be successfully estimated with high accuracy in three dimensions using the proposed method.

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“…In this paper, SVM is used for classification of multi-class (MI) EEG signals [44]. In proposed approach, different parameters can be assigned appropriate values for improving its performance [45]. The values of regularization parameter (C), gamma ( ) and degree of kernel (d) are selected to control the trade-off between number of non-separable points and complexity of algorithm.…”

Section: Support Vector Machinementioning

confidence: 99%

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Ghumman,

Singh

2020

IJEAT

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Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

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A noninvasive brain–computer interface approach for predicting motion intention of activities of daily living tasks for an upper-limb wearable robot

Cited by 19 publications

References 30 publications

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

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