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.

• 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