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