Signal processing and machine learning for brain-machine interfaces
Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the brain to the external world via mapping, assisting, augmenting or repairing human cognitive or sensory-...
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| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Stevenage, United Kingdom :
Institution of Engineering and Technology,
2018.
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| Series: | IET control, robotics and sensors series ;
114. |
| Subjects: | |
| ISBN: | 9781785613999 1785613995 9781523119837 1523119837 9781785613982 1785613987 |
| Physical Description: | 1 online resource : illustrations |
Cover
Table of contents
| LEADER | 06523cam a2200541 i 4500 | ||
|---|---|---|---|
| 001 | kn-on1054199219 | ||
| 003 | OCoLC | ||
| 005 | 20240717213016.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 180925s2018 enka ob 001 0 eng d | ||
| 040 | |a LGG |b eng |e rda |e pn |c LGG |d LGG |d UIU |d STF |d OCLCO |d CDN |d OCLCF |d YDX |d EBLCP |d MERUC |d N$T |d RB# |d UAB |d UMR |d KNOVL |d CEF |d MERER |d CUV |d OCLCO |d OCLCQ |d VLB |d UKAHL |d OCLCQ |d K6U |d OCLCQ |d UX1 |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO |d OCLCL |d SXB | ||
| 020 | |a 9781785613999 |q (electronic bk.) | ||
| 020 | |a 1785613995 |q (electronic bk.) | ||
| 020 | |a 9781523119837 |q (electronic bk.) | ||
| 020 | |a 1523119837 |q (electronic bk.) | ||
| 020 | |z 9781785613982 | ||
| 020 | |z 1785613987 | ||
| 035 | |a (OCoLC)1054199219 | ||
| 245 | 0 | 0 | |a Signal processing and machine learning for brain-machine interfaces / |c edited by Toshihisa Tanaka and Mahnaz Arvaneh. |
| 264 | 1 | |a Stevenage, United Kingdom : |b Institution of Engineering and Technology, |c 2018. | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a IET Control, Robotics and Sensors series ; |v 114 | |
| 504 | |a Includes bibliographical references and index. | ||
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the brain to the external world via mapping, assisting, augmenting or repairing human cognitive or sensory-motor functions. In this book an international panel of experts introduce signal processing and machine learning techniques for BMI/BCI and outline their practical and future applications in neuroscience, medicine, and rehabilitation, with a focus on EEG-based BMI/BCI methods and technologies. Topics covered include discriminative learning of connectivity pattern of EEG; feature extraction from EEG recordings; EEG signal processing; transfer learning algorithms in BCI; convolutional neural networks for event-related potential detection; spatial filtering techniques for improving individual template-based SSVEP detection; feature extraction and classification algorithms for image RSVP based BCI; decoding music perception and imagination using deep learning techniques; neurofeedback games using EEG-based Brain-Computer Interface Technology; affective computing system and more. | ||
| 505 | 0 | |a Intro; Contents; Preface; 1. Brain-computer interfaces and electroencephalogram: basics and practical issues / Mahnaz Arvaneh and Toshihisa Tanaka; Abstract; 1.1 Introduction; 1.2 Core components of a BMI system; 1.3 Signal acquisition; 1.3.1 Electroencephalography; 1.3.2 Positron emission tomography; 1.3.3 Magnetoencephalography; 1.3.4 Functional magnetic resonance imaging; 1.3.5 Near-infrared spectroscopy; 1.3.6 Commonly used method in BMI-why EEG?; 1.4 Measurement of EEG; 1.4.1 Principle of EEG; 1.4.2 How to measure EEG; 1.4.3 Practical issues | |
| 505 | 8 | |a 1.5 Neurophysiological signals in EEG for driving BMIs1.5.1 Evoked potentials; 1.5.2 Spontaneous signals; 1.6 Commonly used EEG processing methods in BMI; 1.6.1 Preprocessing; 1.6.2 Re-referencing; 1.6.3 Feature extraction; 1.6.4 Classification; 1.7 Feedback; 1.8 BMI applications; 1.9 Summary; References; 2. Discriminative learning of connectivity pattern of motor imagery EEG / Xinyang Li, Cuntai Guan, and Huijuan Yang; Abstract; 2.1 Introduction; 2.2 Discriminative learning of connectivity pattern of motor imagery EEG; 2.2.1 Spatial filter design for variance feature extraction | |
| 505 | 8 | |a 2.2.2 Discriminative learning of connectivity pattern2.3 Experimental study; 2.3.1 Experimental setup and data processing; 2.3.2 Correlation results; 2.3.3 Classification results; 2.4 Relations with existing methods; 2.5 Conclusion; References; 3. An experimental study to compare CSP and TSM techniques to extract features during motor imagery tasks / Matteo Sartori, Simone Fiori, and Toshihisa Tanaka; Abstract; 3.1 Introduction; 3.2 Theoretical concepts and methods; 3.2.1 Averaging techniques of SCMs; 3.2.2 SCM averages in CSP and TSM methods; 3.2.3 Multidimensional scaling (MDS) algorithm | |
| 505 | 8 | |a 3.3 Experimental results3.3.1 Classification accuracy; 3.3.2 SCMs distributions on tangent spaces; 3.4 Conclusions; References; 4. Robust EEG signal processing with signal structures / Hiroshi Higashi and Toshihisa Tanaka; Abstract; 4.1 Introduction; 4.2 Source analysis; 4.3 Regularization; 4.4 Filtering in graph spectral domain; 4.4.1 Graph Fourier transform; 4.4.2 Smoothing and dimensionality reduction by GFT; 4.4.3 Tangent space mapping from Riemannian manifold; 4.4.4 Smoothing on functional brain structures; 4.5 Conclusion; References | |
| 505 | 8 | |a 5. A review on transfer learning approaches in brain-computer interface / Ahmed M. Azab, Jake Toth, Lyudmila S. Mihaylova, and Mahnaz ArvanehAbstract; 5.1 Introduction; 5.2 Transfer learning; 5.2.1 History of transfer learning; 5.2.2 Transfer learning definition; 5.2.3 Transfer learning categories; 5.3 Transfer learning approaches; 5.3.1 Instance-based transfer learning; 5.3.2 Feature-representation transfer learning; 5.3.3 Classifier-based transfer learning; 5.3.4 Relational-based transfer learning; 5.4 Transfer learning methods used in BCI; 5.4.1 Instance-based transfer learning in BCI | |
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Brain-computer interfaces. | |
| 650 | 0 | |a Decoders (Electronics) | |
| 650 | 0 | |a Electroencephalography. | |
| 650 | 0 | |a Medical technology. | |
| 650 | 0 | |a Signal processing. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Tanaka, Toshihisa |c (Engineer), |e editor. |1 https://id.oclc.org/worldcat/entity/E39PCjC74gJyt7qbcM6hbHchgX | |
| 700 | 1 | |a Arvaneh, Mahnaz, |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Signal processing and machine learning for brain-machine interfaces. |d London, United Kingdom : Institution of Engineering and Technology, 2018 |z 1785613987 |w (DLC) 2018400763 |w (OCoLC)1030592734 |
| 830 | 0 | |a IET control, robotics and sensors series ; |v 114. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpSPMLBMI2/signal-processing-and?kpromoter=marc |y Full text |
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