Progress in EEG-Based Brain Robot Interaction Systems

Mao, Xiaoqian; Li, Mengfan; Li, Wei; Niu, Linwei; Xian, Bin; Zeng, Ming; Chen, Genshe

doi:10.1155/2017/1742862

Cited by 56 publications

(29 citation statements)

References 199 publications

(164 reference statements)

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“…Mainly, 10,000 grasp instances were recorded and a correlation between the property of the object and the grasp type was established. In reference to [13,14], various analysis of EEG were conducted using some time and frequency analysis approaches. In [15], Feix et al, investigated task classification according to degrees of freedom, required force, and the functional task type.…”

Section: Biomimetic Engineering For Eeg Applicationsmentioning

confidence: 99%

Biomimetic Based EEG Learning for Robotics Complex Grasping and Dexterous Manipulation

Mattar¹,

Al-Junaid²,

Al-Seddiqi³

2018

Biomimetic Prosthetics

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There have been tremendous efforts to understand the biological nature of human grasping, in such a way that it can be learned and copied to prosthesis-robotics and dextrous grasping applications. Several biomimetic methods and techniques have been adopted, hence applied to analytically comprehend ways human performs grasping to duplicate human knowledge. A major topic for further study, is related to decoding the resulting EEG brainwaves during motorizing of fingers and moving parts. To accomplish this, there are a number of phases that are performed, including recording, pre-processing, filtration, and understanding of the waves. However, there are two important phases that have received substantial research attentions. The classification and decoding, of such massive and complex brain waves, as they are two important steps towards understanding patterns during grasping. In this respect, the fundamental objective of this research is to demonstrate how to employ advanced pattern recognition methods, like fuzzy c-mean clustering for understanding resulting EEG brain waves, in such a way to control a prosthesis or robotic hand, while relying sets of detected EEG brainwaves. There are a number of decoding and classification methods and techniques, however we shall look into fuzzy based clustering blended with principle component analysis (PAC) technique to help for the decoding mechanism. EEG brainwaves during a grasping and manipulation have been used for this analysis. This involves, movement of almost five fingers during a grasping defined task. The study has found that, it is not a straight forward task to decode all human fingers motions, as due to the complexity of grasping tasks. However, the adopted analysis was able to classify and identify the different narrowly performed and related fundamental events during a simple grasping task.

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Section: Biomimetic Engineering For Eeg Applicationsmentioning

confidence: 99%

Biomimetic Based EEG Learning for Robotics Complex Grasping and Dexterous Manipulation

Mattar¹,

Al-Junaid²,

Al-Seddiqi³

2018

Biomimetic Prosthetics

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“…There have been many prior studies utilising humanoid robots with EEG signals for various BCI applications [1], [4], [15]. In this section we will focus on the studies making use of SSVEP within this context.…”

Section: Related Workmentioning

confidence: 99%

“…SSVEP is a type of stimulus-evoked neurophysiological response induced simply via subject fixation (or even just via peripheral attention) on visual stimuli and requires almost no a priori user training [11]- [13]. The human cortical signals in the primary visual areas oscillate when visually evoked via these stimuli by a continuously fluctuating sinusoidal cycle [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

Aznan

Connolly

Moubayed

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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2019) 'Using variable natural environment brain-computer interface stimuli for real-time humanoid robot navigation.any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.Abstract-This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time object detection and dry-Electroencephalography (EEG) based human cortical brain bio-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential (SSVEP) stimuli generated directly from the environment the robot is navigating. To these ends, we propose the use of novel variable BCI stimuli by utilising the real-time video streamed via the on-board robot camera as visual input for SSVEP, where the CNN detected natural scene objects are altered and flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are not akin to traditional stimulias both the dimensions of the flicker regions and their on-screen position changes depending on the scene objects detected. Onscreen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly into teleoperation robot commands (approach object, move in a specific direction: right, left or back). This SSVEP decoding model is trained via a priori offline experimental data in which very similar visual input is present for all subjects. The resulting classification demonstrates high performance with mean accuracy of 85% for the real-time robot navigation experiment across multiple test subjects.

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“…With the booming of Internet of Things (IoTs), sensors have been widely used in HAR applications, due to the advantages of no need to deploy in advance, smaller data volume, lower cost, and power consumption. Sensors-based HAR stands out among various technologies [1–3] and has been drawing tremendous attention and applied into a variety people centric application areas, such as medical care [1], emergency rescue [2], and smart home surveillance [3]. …”

Section: Introductionmentioning

confidence: 99%

“…However, accurate probability is difficult to be obtained due to the complexity and subjectivity of human motions, as well as the requirement of large amounts of actual data. Motions are performed under different environments simultaneously, such as applications in medical care and emergency rescue [1, 2, 14]. …”

Section: Introductionmentioning

confidence: 99%

Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

Zhang

et al. 2018

Computational Intelligence and Neuroscience

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Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature and temporal information of human motions. Consequently, they suffer from data dependencies and encounter the curse of dimension and the overfitting issue. Their models are hard to be intuitively understood. Given a specific motion set, if structured domain knowledge could be manually obtained, it could be used for better recognizing certain motions. In this study, we start from a deep analysis on natural physical properties and temporal recurrent transformation possibilities of human motions and then propose a useful Recurrent Transformation Prior Knowledge-based Decision Tree (RT-PKDT) model for recognition of specific human motions. RT-PKDT utilizes temporal information and hierarchical classification method, making the most of sensor streaming data and human knowledge to compensate the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as SVM, BP neural networks, and Bayesian Network, obtaining an accuracy of 96.68%.

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Progress in EEG-Based Brain Robot Interaction Systems

Cited by 56 publications

References 199 publications

Biomimetic Based EEG Learning for Robotics Complex Grasping and Dexterous Manipulation

Biomimetic Based EEG Learning for Robotics Complex Grasping and Dexterous Manipulation

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

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