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检索条件"机构=Laboratory for Neural Interface and Brain Computer Interface"

共 191 条记录，以下是171-180 订阅

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Comparative study of band-power extraction techniques for Motor Imagery classification

Comparative study of band-power extraction techniques for Mo...

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IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and brain

作者： Brodu, Nicolas Lotte, Fabien Lécuyer, Anatole Bât 22 Campus de Beaulieu University of Rennes 1 35042 Rennes Cedex France Signal Processing Department Brain-Computer Interface Laboratory 1 Fusionopolis way 138632 Singapore Singapore INRIA Campus de Beaulieu Avenue du Général Leclerc 35042 Rennes Cedex France

ISBN: (纸本)9781424498918

We review different techniques for extracting the power information contained in frequency bands in the context of electroencephalography (EEG) based brain-computer interfaces (BCI). In this domain it is common to apply only one algorithm for extracting the power information. However previous work and our current study confirm that one may indeed expect varying degrees of success by choosing inadequate algorithms for the power extraction. Our results suggest that on average one algorithm seems superior for extracting the power information for Motor Imagery tasks : the application of a Morlet wavelet on the raw EEG signals, with the time-frequency resolution tradeoff selected by cross-validation. © 2011 IEEE.

关键词： Electroencephalography

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Comparative study of band-power extraction techniques for Motor Imagery classification

Comparative study of band-power extraction techniques for Mo...

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IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and brain (CCMB)

作者： Nicolas Brodu Fabien Lotte Anatole Lécuyer LTSI University of Rennes I Rennes France Institute for Infocomm Research (I2R) Signal Processing Department Brain-Computer Interface Laboratory Singapore INRIA Rennes France

关键词： Time frequency analysis Feature extraction Data mining Estimation Spectrogram brain computer interfaces Accuracy

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Preliminary characterization of a glucose-sensitive hydrogel

Preliminary characterization of a glucose-sensitive hydrogel

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Brooke L. Beier Eric M. Brandner Katherine M. Musick Akira Matsumoto Alyssa Panitch Eric A. Nauman Pedro P. Irazoqui Brain Computer Interface Laboratory and the Weldon School of Biomedical Engineering Purdue University West Lafayette IN USA Weldon School of Biomedical Engineering Purdue University West Lafayette IN USA Department of Bioengineering University of Tokyo Tokyo Japan Weldon School of Biomedical Engineering and School of Mechanical Engineering Purdue University West Lafayette IN USA Brian Computer Interface Laboratory and the Weldon School of Biomedical Engineering Purdue University West Lafayette IN USA

We present proof-of-concept studies that display the potential for using a glucose-sensitive hydrogel as a continuous glucose sensor. A study to characterize the swelling ratio of the hydrogel at normal physiological and pathological hyperglycemic glucose levels was performed. The hydrogel exposed to the hyperglycemic glucose solution had a higher equilibrium swelling ratio than the hydrogel exposed to the normal glucose concentration solution. The diffusivity of a small molecule, fluorescein isothiocyanate (FITC), through a hydrogel exposed to a hyperglycemic solution was determined using fluorescence recovery after photobleaching (FRAP). The diffusivity was found to be 4.2 × 10 -14 m 2 /s, a value approximately four orders of magnitude smaller than the diffusivity of FITC in glucose solution. The permeability of the hydrogel after equilibration in a hyperglycemic solution was found to be 5.1 × 10 -17 m 2 , in the range of 2-4% agarose gels.

关键词： Sugar Diabetes Permeability Monitoring Bleaching Biomedical engineering Fluorescence

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System architecture for stiffness control in brain-machine interfaces

System architecture for stiffness control in brain-machine i...

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作者： Héliot, Rodolphe Orsborn, Amy L. Ganguly, Karunesh Carmena, Jose M. Department of Electrical Engineering and Computer Sciences University of California Berkeley CA 94720 United States University of California San Francisco United States University of California Berkeley United States Joint Graduate Group in Bioengineering University of California Berkeley CA 94729 United States Brain-Machine Interface Systems Laboratory Department of Electrical Engineering and Computer Sciences University of California Berkeley CA 94720 United States Neurology and Rehabilitation Service San Francisco VA Medical Center San Francisco CA 94121 United States Department of Electrical Engineering and Computer Sciences Helen Wills Neuroscience Institute University of California Berkeley CA 94720 United States Department of Neurology University of California San Francisco CA 94143 United States

brain-machine interfaces (BMIs) provide a versatile tool for rehabilitation of severely disabled people. Current BMI systems focus on the control of kinematic variables. However, this approach limits the application space of BMI technology to simulated environments. Real-world rehabilitation robots, on the other hand, must operate in a variety of complex physical situations. BMI systems that are aimed toward prostheses must, then, control interaction forces with their environments. In this paper, we design a BMI-driven architecture that provides a critical link between neuronal ensemble activity and real-world dynamics. In particular, our system allows simultaneous estimation of kinematic and stiffness variables of a prosthetic device. This approach is achieved by recording instantaneous activities from cortical neural ensembles that input to a musculoskeletal model of the arm, from which limb kinematics and dynamics are estimated and converted into control signals of a prosthetic device. Using real neural and behavioral data from nonhuman primates, we show that our architecture can accurately predict kinematic and stiffness variables in different dynamic situations. This architecture has strong implications in the development of the next generation of neural prosthetics that will restore motor function in neurologically impaired patients. Moreover, it demonstrates a novel framework for studying how the brain learns and adapts to new environments. © 2010 IEEE.

关键词： Kinematics

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Feature extraction of EEG during motor imagery and cognition by using morphological MRA

Feature extraction of EEG during motor imagery and cognition...

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ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009

作者： Yamaguchi, Tomonari Fujio, Mitsuhiko Inoue, Katsuhiro Pfurtscheller, Gert Department of Systems Design and Informatics Kyushu Institute of Technology Fukuoka Japan Laboratory of Brain-Computer Interface Graz University of Technology Graz Austria

ISBN: (纸本)9784907764333

Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. Recently, we have proposed the detection method of Error Potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract features concerned with motor imagery and cognition simultaneously from EEG signals. Morphological filter is composed of nonlinear operation between signal and structural function. We propose some design methods of structural function that decide the filter characteristic of morphology. These algorithms are compared to DWT from the view point of filter characteristics. Consequently, effectiveness of our method is confirmed. © 2009 SICE.

关键词： brain computer interface

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Design method of morphological structural function for pattern recognition of EEG signals during motor imagery and cognition

Design method of morphological structural function for patte...

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2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009

作者： Yamaguchi, Tomonari Fujio, Mitsuhiko Inoue, Katsuhiro Pfurtscheller, Gert Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka 820-8502 Japan Laboratory of Brain-Computer Interface Graz University of Technology Graz Austria

ISBN: (纸本)9780769538730

Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen (such system is called BCI). Recently, we have proposed the detection method of Error Potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract features concerned with motor imagery and cognition simultaneously from the EEG signals. Morphological filter is composed of nonlinear operation between signal and structural function and this multi-resolution analysis can be constructed by repeating this filtering to signal while changing structural function. This method is a kind of discrete wavelet analysis with non-linear characteristics and is effective to extract specific shapes. The structural function which decides the filter characteristic is designed to obtain optimal separation based on mutual information algorithm or spectrum dividing algorithm. © 2009 IEEE.

关键词： Multiresolution analysis

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An on-line BCI system for hand movement control using real-time recurrent probabilistic neural network

An on-line BCI system for hand movement control using real-t...

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International IEEE/EMBS Conference on neural Engineering, CNE

作者： Mohammad Ahmadi Abbas Erfanian Brain-Computer Interface Laboratory Neural Technology Research Center Department of Biomedical Engineering Iran University of Science and Technology Tehran Iran

This paper presents a new EEG-based brain-computer interface (BCI) for on-line controlling the hand movement in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. For this purpose, two classifiers are designed. The first classifier which is based on the imagination of right-hand movement is for controlling the hand grasping, holding and opening. The second classifier, which is based on the imagination of left-hand movement is designed for error correction and activating the first classifier. One important issue in developing an on-line BCI is the robust and accurate classification of EEG signal which is characterized with a time-varying distribution. In this work, we present a real-time recurrent probabilistic neural network for classifying the EEG signals. The results show that the subjects were able to achieve an accuracy more than 80% during the first session of experiment without off-line training and 73%-91% during the last session using single-trial classification with no adaptation.

关键词： Control systems Real time systems neural networks Recurrent neural networks Biological neural networks Electroencephalography brain computer interfaces Virtual reality Grasping Error correction

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Comparison of designs towards a subject-independent brain-computer interface based on motor imagery

Comparison of designs towards a subject-independent brain-co...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Fabien Lotte Cuntai Guan Kai Keng Ang Signal Processing Department Brain-Computer Interface Laboratory Institute of Infocomm Research-Agency for Science Singapore Signal Processing Department Brain-Computer Interface Laboratory Institute for Infocomm Research Singapore

A major limitation of current brain-computer interfaces (BCI) based on Motor Imagery (MI) is that they are subject-specific BCI, which require data recording and system training for each new user. This process is time consuming and inconvenient, especially for casual users or portable BCI with limited computational resources. In this paper, we explore the design of a Subject-Independent (SI) MI-based BCI, i.e., a BCI that can be used immediately by any new user without training the BCI with the user's data. This is achieved by training the BCI on data acquired from several other subjects. In order to assess the possibility to build such a BCI, we compared several designs based on different features and classifiers, on data from 9 subjects. Our results suggested that linear classifiers were the most appropriate for the design of MI-based SI-BCI. We also proposed a filter bank common spatial patterns feature extraction method based on a multi-resolution frequency decomposition which achieved the highest accuracy.

关键词： brain computer interfaces Feature extraction Electroencephalography Frequency Filter bank Design methodology computer interfaces USA Councils Portable computers Machine learning

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Design Method of Morphological Structural Function for Pattern Recognition of EEG Signals During Motor Imagery and Cognition

Design Method of Morphological Structural Function for Patte...

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International Conference on Innovative Computing, Information and Control (ICICIC)

作者： Tomonari Yamaguchi Mitsuhiko Fujio Katsuhiro Inoue Gert Pfurtscheller Kyushu Institute of Technology Fukuoka Japan Laboratory of Brain-Computer Interface Graz University of Technology Graz Austria

Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen (such system is called BCI). Recently, we have proposed the detection method of error potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract features concerned with motor imagery and cognition simultaneously from the EEG signals. Morphological filter is composed of nonlinear operation between signal and structural function and this multi-resolution analysis can be constructed by repeating this filtering to signal while changing structural function. This method is a kind of discrete wavelet analysis with non-linear characteristics and is effective to extract specific shapes. The structural function which decides the filter characteristic is designed to obtain optimal separation based on mutual information algorithm or spectrum dividing algorithm.

关键词： Design methodology Pattern recognition Electroencephalography Cognition Feature extraction Signal analysis Filters computer errors Image analysis Filtering

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Feature extraction of EEG during motor imagery and cognition by using morphological MRA

Feature extraction of EEG during motor imagery and cognition...

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SICE Annual Conference

作者： Tomonari Yamaguchi Mitsuhiko Fujio Katsuhiro Inoue Gert Pfurtscheller Department of Systems Design and Informatics Kyushu Institute of Technology Fukuoka Japan Laboratory of Brain-Computer Interface Graz University of Technology Graz Austria

关键词： Feature extraction Electroencephalography Cognition Filters brain computer interfaces Communication channels computer errors Image analysis Signal analysis Design methodology

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