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Machine-learning techniques for EEG data

Machine-learning techniques for EEG data

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In this chapter, we present an introductory overview of machine-learning techniques that can be used to recognize mental states from electroencephalogram (EEG) signals in brain-computer interfaces (BCIs). More particularly, we discuss how to extract relevant and robust information from noisy EEG signals. Due to the spatial properties of the EEG acquisition modality, learning robust spatial filters is a crucial step in the analysis of EEG signals. Optimal spatial filters will help us extract relevant and robust features, helping considerably the subsequent recognition of mental states. Also, a few classification algorithms are presented to assign this information into a mental state. Furthermore, particular care will be given on algorithms and techniques related to steady-state visual evoked potentials (SSVEPs) BCI and sensorimotor rhythms (SMRs) BCI systems. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyze EEG signals.

Chapter Contents:

  • 7.1 Introduction
  • 7.1.1 What is the EEG signal?
  • 7.1.2 EEG-based BCI paradigms
  • 7.1.2.1 SSVEP paradigm
  • 7.1.2.2 SMR paradigm
  • 7.1.2.3 General properties of an EEG dataset
  • 7.1.3 What is machine learning?
  • 7.1.4 What do you want to learn in EEG analysis for BCI application?
  • 7.2 Basic tools of supervised learning in EEG analysis
  • 7.2.1 Generalized Rayleigh quotient function
  • 7.2.2 Linear regression modeling
  • 7.2.3 Maximum likelihood (ML) parameter estimation
  • 7.2.4 Bayesian modeling of machine learning
  • 7.3 Learning of spatial filters
  • 7.3.1 Canonical correlation analysis
  • 7.3.2 Common spatial patterns
  • 7.4 Classification algorithms
  • 7.4.1 Linear discriminant analysis
  • 7.4.2 Least squares classifier
  • 7.4.3 Bayesian LDA
  • 7.4.4 Support vector machines
  • 7.4.5 Kernel-based classifier
  • 7.5 Future directions and other issues
  • 7.5.1 Adaptive learning
  • 7.5.2 Transfer learning and multitask learning
  • 7.5.3 Deep learning
  • 7.6 Summary
  • References

Inspec keywords: electroencephalography; learning (artificial intelligence); spatial filters; brain-computer interfaces; visual evoked potentials; medical signal processing

Other keywords: machine-learning techniques; relevant information; robust spatial filters; introductory overview; robust information; noisy EEG signals; relevant features; optimal spatial filters; spatial properties; robust features; electroencephalogram signals; brain-computer interfaces; particular care; mental state

Subjects: Digital signal processing; Knowledge engineering techniques; User interfaces; Signal processing and detection; Computer assistance for persons with handicaps; Electrodiagnostics and other electrical measurement techniques; Electrical activity in neurophysiological processes; Computer vision and image processing techniques; Bioelectric signals; Biology and medical computing

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