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2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
This paper presents a novel method, based on multi-channel Empirical Mode Decomposition (EMD), of classifying the electroencephalogram (EEG) recordings of imagined movement by a subject within a brain-computer interfacing (BCI) framework. EMD is a technique that divides any non-linear or non-stationary signal into groups of frequency harmonics, called ...
Simon R H, Davies, Christopher J, James
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This paper presents a novel method, based on multi-channel Empirical Mode Decomposition (EMD), of classifying the electroencephalogram (EEG) recordings of imagined movement by a subject within a brain-computer interfacing (BCI) framework. EMD is a technique that divides any non-linear or non-stationary signal into groups of frequency harmonics, called ...
Simon R H, Davies, Christopher J, James
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Design and Research on Human-Computer Interactive Interface of Navigation Robot in the IOT Mode
2018The display design methods in the background of media convergence is improving gradually, and the navigation robot is applied to the public environment as a new kind of display assistant method, which can better meet visitors’ needs and enhance their emotional experience to the exhibition.
Ye Zhang, Bingmei Bie, Rongrong Fu
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Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2015
This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system.
Jinjia, Wang, Yuan, Liu
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This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system.
Jinjia, Wang, Yuan, Liu
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Biomedizinische Technik. Biomedical engineering, 2006
Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design.
Gernot R, Müller-Putz +3 more
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Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design.
Gernot R, Müller-Putz +3 more
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A computational approach of interface and phase-field mixed mode fracture
AIP Conference Proceedings, 2023openaire +1 more source
2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), 2022
Chenxi Chu +4 more
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Chenxi Chu +4 more
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Flexible Electrodes for Brain–Computer Interface System
Advanced Materials, 2023Tengjiao Wang, Peng LI, Wei Huang
exaly