This book introduces 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.
This book introduces 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.
* 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
* 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
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