| Description |
1 online resource : illustrations. |
| Physical Medium |
polychrome |
| Description |
text file |
| Series |
IET Control, Robotics and Sensors series ; 114
|
|
IET control, robotics and sensors series ; 114.
|
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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. |
| Contents |
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 |
|
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 |
|
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 |
|
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 |
|
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 |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Brain-computer interfaces.
|
|
Brain-computer interfaces. |
|
Decoders (Electronics)
|
|
Decoders (Electronics) |
|
Electroencephalography.
|
|
Electroencephalography. |
|
Medical technology.
|
|
Medical technology. |
|
Signal processing.
|
|
Signal processing. |
| Genre/Form |
Electronic books.
|
| Added Author |
Tanaka, Toshihisa (Engineer), editor.
|
|
Arvaneh, Mahnaz, editor.
|
| Other Form: |
Print version: Signal processing and machine learning for brain-machine interfaces. London, United Kingdom : Institution of Engineering and Technology, 2018 1785613987 (DLC) 2018400763 (OCoLC)1030592734 |
| ISBN |
9781785613999 (electronic book) |
|
1785613995 (electronic book) |
|
9781523119837 (electronic book) |
|
1523119837 (electronic book) |
|
9781785613982 |
|
1785613987 |
|
