An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles

Li, Mengfan; Li, Wei; Niu, Linwei; Zhou, Huihui; Chen, Genshe; Duan, Feng

doi:10.1109/tie.2016.2538740

Cited by 19 publications

(5 citation statements)

References 42 publications

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“…that correspond to particular cognition states [16], which can serve as natural biomarkers of the user's intention of interaction. This special characteristic of ERP has been evaluated for device control [17], target and error detection [18], [19], and so on. As a potential substitute for the "click" operation in HCI system, accumulating evidences confirmed that, when an intention of item selection emerged, negative potentials could be detected during conscious dwell time in the central [17] and parietal [20], [21] electrodes.…”

mentioning

confidence: 99%

“…This special characteristic of ERP has been evaluated for device control [17], target and error detection [18], [19], and so on. As a potential substitute for the "click" operation in HCI system, accumulating evidences confirmed that, when an intention of item selection emerged, negative potentials could be detected during conscious dwell time in the central [17] and parietal [20], [21] electrodes. One of the most popular paradigm in the EEG-based HCI research is P300 speller, which utilizes the uncommon event (flash of the target character) to induce P300 wave and decode user's intention [19].…”

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confidence: 99%

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Improving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Data

Xianliang¹,

Pan²,

Wang³

et al. 2021

Preprint

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Intention decoding is an indispensable procedure in hands-free human-computer interaction (HCI). Conventional eye-tracking system using single-model fixation duration possibly issues commands ignoring users' real expectation. In the current study, an eye-brain hybrid brain-computer interface (BCI) interaction system was introduced for intention detection through fusion of multi-modal eye-track and ERP (a measurement derived from EEG) features. Eye-track and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25 icons. The corresponding fixation duration of eye-tracking and ERP were extracted. Five previously-validated LDA-based classifiers (including RLDA, SWLDA, BLDA, SKLDA, and STDA) and the widely-used CNN method were adopted to verify the efficacy of feature fusion from both offline and pseudo-online analysis, and optimal approach was evaluated through modulating the training set and system response duration. Our study demonstrated that the input of multi-modal eye-track and ERP features achieved superior performance of intention detection in the single trial classification of active search task. And compared with singlemodel ERP feature, this new strategy also induced congruent accuracy across different classifiers. Moreover, in comparison with other classification methods, we found that the SKLDA exhibited the superior performance when fusing feature in offline test (ACC=0.8783, AUC=0.9004) and online simulation with different sample amount and duration length. In sum, the current study revealed a novel and effective approach for intention classification using eye-brain hybrid BCI, and further supported the real-life application of hands-free HCI in a more precise and stable manner.

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confidence: 99%

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confidence: 99%

Improving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Data

Xianliang¹,

Pan²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, the proposed brain-controlled vehicle may provide an alternative way for some subjects who have a relatively low accuracy in using the brain-controlled vehicle developed in [ 1 ]. For other brain-controlled systems, such as telepresence controlling an NAO humanoid robot in [ 28 ], the robot moved slowly and discontinuously and was not controlled in the first perspective. The comparison between this work and the related articles is also shown below.…”

Section: Resultsmentioning

confidence: 99%

“…Although some studies explored how to use P300 potentials to control a wheelchair or humanoid robot by issuing motion control commands [ 27 , 28 ], the former are task-level controlled systems, and the latter move slowly and discontinuously. Furthermore, controlling a vehicle using EEG signals is more challenging than controlling a mobile robot since brain-controlled vehicles are more complicated due to their dynamic characteristics and travel at higher speeds [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Asynchronous Brain Signals-Based Driver–Vehicle Interface for Brain-Controlled Vehicles

Lian,

Guo,

Qiao

et al. 2023

Bioengineering

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Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver–vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver–vehicle interfaces, multimodal interaction, and intelligent vehicles.

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“…Due to their simple system configuration and advantages in safety and cost [3,4], electroencephalograph (EEG)-based BCIs have been extensively studied by researchers, and have been applied in medical diagnosis, disability assistance, smart homes, entertainment, and many other fields [5][6][7][8][9]. At present, commonly used EEG signals include Motor Imaging (MI) [10,11], Slow Cortical Potential (SCP) [12], Event Related Potential (ERP) [13,14], motion-onset Visual Evoked Potential (mVEP) [15,16], and Steady-State Visual Evoked Potential (SSVEP) [7,17]. Compared with other methods, the system configuration of SSVEP is simpler, and more stable signals and higher recognition accuracy can be obtained without additional training for the subjects [18,19].…”

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confidence: 99%

Multivariate weighted recurrent network for analyzing SSMVEP signals from EEG literate and illiterate

Gao

Zhou

Yang

et al. 2019

EPL

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Recently, the Steady-State Motion Visual Evoked Potential (SSMVEP)-based Brain Computer Interface (BCI) has attracted a lot of attention. We design a SSMVEP-based BCI, in which a ring-shaped motion checkerboard pattern is used to realize SSMVEP stimulation. In particular, we firstly conduct SSMVEP experiments to obtain electroencephalogram (EEG) signals from 10 subjects, including 5 EEG literates and 5 EEG illiterates. By using the Canonical Correlation Analysis (CCA) and Support Vector Machine (SVM) method, we find that the classification accuracies of EEG illiterates are relatively lower than that of EEG literates. Thus, in order to investigate the differences in brain cognitive processes between the two groups of subjects, we construct a multivariate weighted recurrence network and analyze the weighted local efficiency and the clustering coefficient of the two groups. The results indicate that in SSMVEP experiment, there are significant differences between the two groups of subjects in these two network indicators. Our approach and analysis provide novel insights into the cognitive behavior of the brain and understanding of the “BCI Illiteracy” problem.

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An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles

Abstract: Abstract-This paper develops an event-related potential (ERP)-

Cited by 19 publications

References 42 publications

Improving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Data

Improving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Data

A Novel Asynchronous Brain Signals-Based Driver–Vehicle Interface for Brain-Controlled Vehicles

Multivariate weighted recurrent network for analyzing SSMVEP signals from EEG literate and illiterate

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