MARC 닫기

• LDR

  00000cam c2200205 c 4500

• 001

  000000587807

• 005

  20200904111253

• 006

  m d

• 007

  cr |n|||||||||

• 008

  180925s2018 enk ob 001 engd

• 020

  ▼a  1785613995

• 020

  ▼a  9781785613999
  ▼q  (electronic bk.)

• 020

  ▼z  1785613987

• 020

  ▼z  9781785613982

• 035

  ▼a  1925309
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1054199219

• 040

  ▼a  LGG
  ▼b  eng
  ▼c  LGG
  ▼d  STF
  ▼d  CDN
  ▼d  LGG
  ▼d  UIU
  ▼d  248023
  ▼d  OCLCO
  ▼d  YDX
  ▼d  OCLCF
  ▼d  EBLCP
  ▼d  MERUC
  ▼d  N$T
  ▼e  pn
  ▼e  rda

• 050

  ▼a  QP360.7
  ▼b  .S54 2018eb

• 082

  ▼a  006.31

• 245

  ▼a  Signal processing and machine learning for brain-machine interfaces /:
  ▼c  edited by Toshihisa Tanaka and Mahnaz Arvaneh.
  ▼h  [electronic resource].

• 260

  ▼a  Stevenage, United Kingdom:
  ▼b  Institution of Engineering and Technology,
  ▼c  2018.

• 300

  ▼a  1 online resource.

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼b  c
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 504

  ▼a  Includes bibliographical references and index.

• 505

  ▼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

  ▼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

  ▼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

  ▼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

  ▼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

• 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.

• 588

  ▼a  Print version record.

• 650

  ▼a  Brain-computer interfaces.

• 650

  ▼a  Decoders (Electronics)

• 650

  ▼a  Electroencephalography.

• 650

  ▼a  Medical technology.

• 650

  ▼a  Signal processing.

• 776

  ▼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
  ▼w  (OCoLC)1030592734

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1925309

• 990

  ▼a  강리원
  ▼b  강리원

• 991

  ▼a  eBook