Signal Processing and Machine Learning for Brain-Machine Interfaces

Toshihisa Tanaka is an Associate Professor at the Department of Electrical and Electronic Engineering of Tokyo University of Agriculture and Technology. He is Co-editor of Signal Processing Techniques for Knowledge Extraction and Information Fusion, and Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, Computational Intelligence and Neuroscience, and Advances in Data Science and Adaptive Analysis. He is also a member-at-large of the board of governors of Asia-Pacific Signal and Information Processing Association (APSIPA), a senior member of the IEEE, and a member of the IEICE and APSIPA.Mahnaz Arvaneh is a Lecturer in the Department of Automatic Control and Systems Engineering and a member of Centre for Assistive Technology and Connected Health (CATCH) at the University of Sheffield, UK. She is an Associate Editor in IEEE Transaction on Neural Systems and Rehabilitation Engineering, as well as a technical committee member for APSIPA and the IEEE Systems, Man, Cybernetics conference. Through her research, she aims to improve our understanding of the human body, both to address fundamental questions in the control of physiological systems and to develop improved therapeutic, assistive, adaptive and rehabilitative technologies for a variety of medical conditions.

• Chapter 1: Brain-computer interfaces and electroencephalogram: basics and practical issuesChapter 2: Discriminative learning of connectivity pattern of motor imagery EEGChapter 3: An experimental study to compare CSP and TSM techniques to extract features during motor imagery tasksChapter 4: Robust EEG signal processing with signal structuresChapter 5: A review on transfer learning approaches in brain-computer interfaceChapter 6: Unsupervised learning for brain-computer interfaces based on event-related potentialsChapter 7: Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain-computer interfaceChapter 8: A BCI challenge for the signal-processing community: considering the user in the loopChapter 9: Feedforward artificial neural networks for event-related potential detectionChapter 10: Signal models for brain interfaces based on evoked response potential in EEGChapter 11: Spatial filtering techniques for improving individual template-based SSVEP detectionChapter 12: A review of feature extraction and classification algorithms for image RSVP-based BCIChapter 13: Decoding music perception and imagination using deep-learning techniquesChapter 14: Neurofeedback games using EEG-based brain-computer interface technology