Signal processing and machine learning for brain-machine interfaces

• Other Authors: Tanaka, Toshihisa (Engineer), (Editor), Arvaneh, Mahnaz, (Editor)
• Format: eBook
• Language: English
• Published: Stevenage, United Kingdom : Institution of Engineering and Technology, 2018.
• Series: IET control, robotics and sensors series ; 114.
• Subjects: Brain-computer interfaces.; Decoders (Electronics); Electroencephalography.; Medical technology.; Signal processing.; elektronické knihy; electronic books
• ISBN: 9781785613999; 1785613995; 9781523119837; 1523119837; 9781785613982; 1785613987
• Physical Description: 1 online resource : illustrations
• Access: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty

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.