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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Tanaka, Toshihisa (Engineer) (Editor ), Arvaneh, Mahnaz (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Stevenage, United Kingdom : Institution of Engineering and Technology, 2018.
Colección:IET control, robotics and sensors series ; 114.
Temas:
Brain-computer interfaces.
Decoders (Electronics)
Electroencephalography.
Medical technology.
Signal processing.
Electroencephalography
Medical Laboratory Science
Interfaces cerveau-ordinateur.
Décodeurs (Électronique)
Électroencéphalographie.
Technologie médicale.
Traitement du signal.
COMPUTERS > General.
Brain-computer interfaces
Medical technology
Signal processing
brain-computer interfaces.
decoding.
electroencephalography.
medical signal processing.
neural net architecture.
spatial filters.
unsupervised learning.
Acceso en línea:Texto completo
Descripción
Sumario: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.
Descripción Física:1 online resource : illustrations
Bibliografía:Includes bibliographical references and index.
ISBN:9781785613999
1785613995
9781523119837
1523119837

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