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180925s2018 enk ob 001 0 eng d |
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|z 1785613987
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|z 9781785613982
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|a 9781785613999
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|a QP360.7
|b .S54 2018
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|a 006.31
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| 245 |
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|a Signal processing and machine learning for brain-machine interfaces /
|c edited by Toshihisa Tanaka and Mahnaz Arvaneh.
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| 264 |
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|a Stevenage, United Kingdom :
|b Institution of Engineering and Technology,
|c 2018.
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a Includes bibliographical references and index.
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|a Machine generated contents note:
|t Abstract /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.1.
|t Introduction /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.2.
|t Core components of a BMI system /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.3.
|t Signal acquisition /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.3.1.
|t Electroencephalography /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.3.2.
|t Positron emission tomography /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.3.3.
|t Magnetoencephalography /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.3.4.
|t Functional magnetic resonance imaging /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.3.5.
|t Near-infrared spectroscopy /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.3.6.
|t Commonly used method in BMI-why EEG? /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.4.
|t Measurement of EEG /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.4.1.
|t Principle of EEG /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.4.2.
|t How to measure EEG /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.4.3.
|t Practical issues /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.5.
|t Neurophysiological signals in EEG for driving BMIs /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.5.1.
|t Evoked potentials /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.5.2.
|t Spontaneous signals /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.6.
|t Commonly used EEG processing methods in BMI /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.6.1.
|t Preprocessing /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.6.2.
|t Re-referencing /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.6.3.
|t Feature extraction /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.6.4.
|t Classification /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.7.
|t Feedback /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.8.
|t BMI applications /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|g 1.9.
|t Summary /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|t References /
|r Toshihisa Tanaka /
|r Mahnaz Arvaneh --
|t Abstract /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.1.
|t Introduction /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.2.
|t Discriminative learning of connectivity pattern of motor imagery EEG /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.2.1.
|t Spatial filter design for variance feature extraction /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.2.2.
|t Discriminative learning of connectivity pattern /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.3.
|t Experimental study /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.3.1.
|t Experimental setup and data processing /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.3.2.
|t Correlation results /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.3.3.
|t Classification results /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.4.
|t Relations with existing methods /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|g 2.5.
|t Conclusion /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|t References /
|r Xinyang Li /
|r Huyuan Yang /
|r Cuntai Guan --
|t Abstract /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.1.
|t Introduction /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.2.
|t Theoretical concepts and methods /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.2.1.
|t Averaging techniques of SCMs /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.2.2.
|t SCM averages in CSP and TSM methods /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.2.3.
|t Multidimensional scaling (MDS) algorithm /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.3.
|t Experimental results /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.3.1.
|t Classification accuracy /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.3.2.
|t SCMs distributions on tangent spaces /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|g 3.4.
|t Conclusions /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|t References /
|r Matteo Sartori /
|r Toshihisa Tanaka /
|r Simone Fiori --
|t Abstract /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.1.
|t Introduction /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.2.
|t Source analysis /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.3.
|t Regularization /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.4.
|t Filtering in graph spectral domain /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.4.1.
|t Graph Fourier transform /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.4.2.
|t Smoothing and dimensionality reduction by GFT /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.4.3.
|t Tangent space mapping from Riemannian manifold /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.4.4.
|t Smoothing on functional brain structures /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|g 4.5.
|t Conclusion /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|t References /
|r Toshihisa Tanaka /
|r Hiroshi Higashi --
|t Abstract /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.1.
|t Introduction /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.2.
|t Transfer learning /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.2.1.
|t History of transfer learning /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.2.2.
|t Transfer learning definition /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.2.3.
|t Transfer learning categories /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.3.
|t Transfer learning approaches /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.3.1.
|t Instance-based transfer learning /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.3.2.
|t Feature-representation transfer learning /
|r Jake Toth /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova --
|g 5.3.3.
|t Classifier-based transfer learning /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.3.4.
|t Relational-based transfer learning /
|r Ahmed M. Azab /
|r Jake Toth /
|r Lyudmila S. Mihaylova /
|r Mahnaz Arvaneh --
|g 5.4.
|t Transfer learning methods used in BCI /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.4.1.
|t Instance-based transfer learning in BCI /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.4.2.
|t Feature-representation transfer learning in BCI /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.4.3.
|t Classifier-based transfer learning in BCI /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.4.4.
|t Unsupervised transfer learning /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.5.
|t Challenges and discussion /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.5.1.
|t Instance-based transfer learning in BCI /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.5.2.
|t Feature-representation transfer learning in BCI /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.5.3.
|t Classifier-based transfer learning in BCI /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|g 5.6.
|t Summary /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|t References /
|r Ahmed M. Azab /
|r Mahnaz Arvaneh /
|r Lyudmila S. Mihaylova /
|r Jake Toth --
|t Abstract /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.1.
|t Introduction /
|r Michael Tangermann /
|r Pieter-Jan Kindermans /
|r Klaus-Robert Muller /
|r Thibault Verhoeven /
|r David Hubner --
|g 6.2.
|t Event-related potential based brain-computer interfaces /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.3.
|t Decoding based on expectation maximisation /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.3.1.
|t probabilistic model for ERP BCI /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.3.2.
|t Training the model /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.4.
|t Decoding based on learning from label proportions /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.4.1.
|t Learning from label proportions /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.4.2.
|t modified ERP paradigm /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.4.3.
|t Training of the LLP model /
|r Pieter-Jan Kindermans /
|r David Hubner /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven --
|g 6.5.
|t Combining EM and LLP decoders analytically /
|r Klaus-Robert Muller /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans /
|r Michael Tangermann --
|g 6.5.1.
|t Training the MIX model /
|r David Hubner /
|r Thibault Verhoeven /
|r Klaus-Robert Muller /
|r Michael Tangermann /
|r Pieter-Jan Kindermans --
|g 6.6.
|t Experimental setup /
|r Klaus-Robert Muller /
|r Michael Tangermann /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans --
|g 6.6.1.
|t Data /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans --
|g 6.6.2.
|t Data processing /
|r Klaus-Robert Muller /
|r Michael Tangermann /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans --
|g 6.6.3.
|t Methods and hyperparameters /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans --
|g 6.7.
|t Results /
|r Klaus-Robert Muller /
|r Michael Tangermann /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans --
|g 6.8.
|t Conclusion /
|r Michael Tangermann /
|r Klaus-Robert Muller /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans --
|t Acknowledgements /
|r Klaus-Robert Muller /
|r Michael Tangermann /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans --
|
| 505 |
0 |
0 |
|a Contents note continued:
|t References /
|r Klaus-Robert Muller /
|r Thibault Verhoeven /
|r David Hubner /
|r Pieter-Jan Kindermans /
|r Michael Tangermann --
|t Abstract /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.1.
|t Introduction /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.2.
|t Background /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.2.1.
|t Covariate shift in EEG signals /
|r Dheeraj Rathee /
|r Haider Raza --
|g 7.2.2.
|t Adaptive learning methods in EEG-based BCI /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.3.
|t Covariate shift detection-based nonstationary adaptation (CSD-NSA) algorithm /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.3.1.
|t Problem formulation /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.3.2.
|t Covariate shift detection (CSD) test /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.3.3.
|t Supervised CSD-NSA algorithm /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.3.4.
|t Unsupervised CSD-NSA algorithm /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.4.
|t Experimental validation of the CSD-NSA algorithms /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.4.1.
|t EEG dataset /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.4.2.
|t Signal processing and feature extraction /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.4.3.
|t Feature selection and parameter estimation /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.4.4.
|t Empirical results /
|r Haider Raza /
|r Dheeraj Rathee --
|g 7.5.
|t Discussion and future prospects /
|r Haider Raza /
|r Dheeraj Rathee --
|t References /
|r Haider Raza /
|r Dheeraj Rathee --
|t Abstract /
|r Fabien Lotte /
|r Camille Jeunet /
|r Jelena Mladenovic /
|r Bernard N'Kaoua /
|r Lea Pillette --
|g 8.1.
|t Introduction /
|r Fabien Lotte /
|r Camille Jeunet /
|r Jelena Mladenovic /
|r Bernard N'Kaoua /
|r Lea Pillette --
|g 8.2.
|t Modeling the user /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.2.1.
|t Estimating and tracking the user's mental states from multimodal sensors /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.2.2.
|t Quantifying users' skills /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.2.3.
|t Creating a dynamic model of the users' states and skills /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.3.
|t Improving BCI user training /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.3.1.
|t Designing features and classifiers that the user can understand and learn from /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.3.2.
|t Identifying when to update classifiers to enhance learning /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.3.3.
|t Designing BCI feedbacks ensuring learning /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|g 8.4.
|t Conclusion /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|t Acknowledgments /
|r Jelena Mladenovic /
|r Bernard N'Kaoua /
|r Camille Jeunet /
|r Fabien Lotte /
|r Lea Pillette --
|t References /
|r Fabien Lotte /
|r Camille Jeunet /
|r Lea Pillette /
|r Bernard N'Kaoua /
|r Jelena Mladenovic --
|t Abstract /
|r Hubert Cecotti --
|g 9.1.
|t Introduction /
|r Hubert Cecotti --
|g 9.2.
|t Event-related potentials /
|r Hubert Cecotti --
|g 9.3.
|t Feedforward neural networks /
|r Hubert Cecotti --
|g 9.3.1.
|t Activation functions /
|r Hubert Cecotti --
|g 9.3.2.
|t Error evaluation /
|r Hubert Cecotti --
|g 9.3.3.
|t Architectures /
|r Hubert Cecotti --
|g 9.4.
|t Methods /
|r Hubert Cecotti --
|g 9.5.
|t Experimental protocol /
|r Hubert Cecotti --
|g 9.5.1.
|t Cony nets /
|r Hubert Cecotti --
|g 9.5.2.
|t Performance evaluation /
|r Hubert Cecotti --
|g 9.6.
|t Results /
|r Hubert Cecotti --
|g 9.7.
|t Discussion /
|r Hubert Cecotti --
|g 9.8.
|t Conclusion /
|r Hubert Cecotti --
|t References /
|r Hubert Cecotti --
|t Abstract /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.1.
|t ERP-based BCIs /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.1.1.
|t Multidimensional EEG classification /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.1.2.
|t Nonstationarities in EEG signals /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.1.3.
|t Noise in the class labels /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.2.
|t ERP-based inference /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r James McLean /
|r Yeganeh M. Marghi /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r Deniz Erdogmus /
|r Bruna Girvent --
|g 10.2.1.
|t ERP detection /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.2.2.
|t Linear model and covariance matrix structures /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.2.3.
|t Nonstationarities detection /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.2.4.
|t Decoupling the class label from ERP detection /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.3.
|t Experimental results and discussions /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.3.1.
|t ERP-based BCI typing system /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r James McLean /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r Deniz Erdogmus --
|g 10.3.2.
|t ERP-based BCI with tactile stimuli /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|g 10.4.
|t Summary /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r James McLean /
|r Yeganeh M. Marghi /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r Deniz Erdogmus /
|r Bruna Girvent --
|t References /
|r Yeganeh M. Marghi /
|r Paula Gonzalez-Navarro /
|r Fernando Quivira /
|r Deniz Erdogmus /
|r Bruna Girvent /
|r Mohammad Moghadamfalahi /
|r Murat Akcakaya /
|r James McLean --
|t Abstract /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.1.
|t Introduction /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.2.
|t Individual template-based SSVEP detection /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.2.1.
|t Basic framework /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.2.2.
|t Ensemble strategy /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.2.3.
|t Filter bank analysis /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.3.
|t Spatial-filtering techniques /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.3.1.
|t Average combination /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.3.2.
|t Minimum energy combination /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.3.3.
|t Canonical correlation analysis /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.3.4.
|t Independent component analysis /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.3.5.
|t Task-related component analysis /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.4.
|t Material and methods /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.4.1.
|t Dataset /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.4.2.
|t Performance evaluation /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.5.
|t Results and discussions /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.5.1.
|t Signal features of SSVEPs after spatial filtering /
|r Yijun Wang /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung --
|g 11.5.2.
|t comparison of frameworks for SSVEP detection /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.5.3.
|t comparison of electrodes settings /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.5.4.
|t Toward further improvement /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.5.5.
|t Challenges and future direction /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|g 11.6.
|t Conclusions /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|t References /
|r Masaki Nakanishi /
|r Tzyy-Ping Jung /
|r Yijun Wang --
|t Abstract /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.1.
|t Introduction /
|r Zhengwei Wang /
|r Graham Healy /
|r Alan F. Smeaton /
|r Tomas E. Ward --
|g 12.2.
|t Overview of RSVP experiments and EEG data /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.2.1.
|t RSVP experiment for EEG data acquisition /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.2.2.
|t Brief introduction to RSVP-EEG pattern /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.2.3.
|t RSVP-EEG data preprocessing and properties /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.2.4.
|t Performance evaluation metrics /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.3.
|t Feature extraction methods used in RSVP-based BCI research /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|
| 505 |
0 |
0 |
|a Contents note continued:
|g 12.3.1.
|t Spatial filtering /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.3.2.
|t Time-frequency representation /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.3.3.
|t Other feature extraction methods /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.3.4.
|t Summary /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.4.
|t Survey of classifiers used in RSVP-based BCI research /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.4.1.
|t Linear classifiers /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.4.2.
|t Neural networks /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|g 12.5.
|t Conclusion /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|t Acknowledgment /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|t References /
|r Zhengwei Wang /
|r Tomas E. Ward /
|r Alan F. Smeaton /
|r Graham Healy --
|t Abstract /
|r Sebastian Stober /
|r Avital Sternin --
|g 13.1.
|t Introduction and motivation /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.1.1.
|t Evidence from research on auditory perception and imagination /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.1.2.
|t Existing auditory and music-based BCIs /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.2.
|t Deep learning for EEG analysis - the state of the art /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.2.1.
|t Challenges /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.2.2.
|t Deep learning applied to EEG analysis /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.2.3.
|t Custom solutions developed for EEG analysis /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.2.4.
|t need for open science /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.2.5.
|t Summary /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.3.
|t Experimental design /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.3.1.
|t Stimulus selection /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.3.2.
|t Equipment and procedure /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.3.3.
|t Preprocessing /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.4.
|t Representation learning techniques for pre-training /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.4.1.
|t Basic auto-encoder /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.4.2.
|t Cross-trial encoder /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.4.3.
|t Hydra-net cross-trial encoder /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.4.4.
|t Similarity-constraint encoder /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.4.5.
|t Siamese networks and triplet networks /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.5.
|t Interpreting trained models /
|r Avital Sternin /
|r Sebastian Stober --
|g 13.6.
|t Conclusions /
|r Avital Sternin /
|r Sebastian Stober --
|t References /
|r Avital Sternin /
|r Sebastian Stober --
|t Abstract /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.1.
|t Introduction /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.2.
|t Generic framework of a neurofeedback game using BCI technology /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.2.1.
|t Data acquisition /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.2.2.
|t Data processing /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.2.3.
|t Control signal generation /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.2.4.
|t Gaming interface /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.3.
|t Classification of neurofeedback games based on BCI interaction /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.3.1.
|t Active BCI games /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.3.2.
|t Reactive BCI games /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.3.3.
|t Passive BCI games /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.3.4.
|t Hybrid games /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.4.
|t EEG devices for neurofeedback development /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.5.
|t Benefits of neurofeedback games /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.5.1.
|t Novel entertainment modality /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.5.2.
|t Cognitive enhancement tool in the neurologically challenged as well as healthy /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.5.3.
|t BCI performance booster /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.6.
|t Challenges in practical implementation /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|g 14.7.
|t Conclusion /
|r Kavitha R. Thomas /
|r A.P. Vinod --
|t References /
|r Kavitha R. Thomas /
|r A.P. Vinod.
|
| 533 |
|
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|a Electronic reproduction.
|b Ann Arbor, MI
|n Available via World Wide Web.
|
| 588 |
0 |
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|a Print version record.
|
| 650 |
|
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|a Brain-computer interfaces.
|
| 650 |
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|a Decoders (Electronics)
|
| 650 |
|
0 |
|a Electroencephalography.
|
| 650 |
|
0 |
|a Medical technology.
|
| 650 |
|
0 |
|a Signal processing.
|
| 710 |
2 |
|
|a ProQuest (Firm)
|
| 776 |
0 |
8 |
|i Print version:
|t SIGNAL PROCESSING AND MACHINE LEARNING FOR BRAIN MACHINE INTERFACES.
|d [S.l.] : INST OF ENGIN AND TECH, 2018
|z 1785613987
|z 9781785613982
|
| 856 |
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|u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=5598348
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