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Signal Processing and Machine Learning for Brain-Machine Interfaces

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Editors: Toshihisa Tanaka; Mahnaz Arvaneh
Publication Year: 2018

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.

Other keywords: unsupervised adaptation; EEG signal decoding; BMI command discrimination; deep learning; supervised adaptation; machine learning; transfer learning; brain-machine interfaces; optimal spatial filtering; supervised connectivity analysis; tangent-space mapping; neural networks; low spatial resolution; parametric modeling; signal processing; signal structures

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