In this chapter, we introduce an architecture for rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) systems that use electroencephalography (EEG). Hereafter, we will refer to the coupling of the RSVP protocol with EEG to support a target-search BCI as RSVP-EEG. Our focus in this chapter is on a review of feature extraction and classification algorithms applied in RSVP-EEG development. We briefly present the commonly deployed algorithms and describe their properties based on the literature. We conclude with a discussion on the future trajectory of this exciting branch of BCI research.

Chapter Contents:

• Abstract
• 12.1 Introduction
• 12.2 Overview of RSVP experiments and EEG data
• 12.2.1 RSVP experiment for EEG data acquisition
• 12.2.2 Brief introduction to RSVP-EEG pattern
• 12.2.3 RSVP-EEG data preprocessing and properties
• 12.2.4 Performance evaluation metrics
• 12.3 Feature extraction methods used in RSVP-based BCI research
• 12.3.1 Spatial filtering
• 12.3.1.1 Supervised spatial filtering
• 12.3.1.2 Unsupervised spatial filtering
• 12.3.2 Time-frequency representation
• 12.3.3 Other feature extraction methods
• 12.3.4 Summary
• 12.4 Survey of classifiers used in RSVP-based BCI research
• 12.4.1 Linear classifiers
• 12.4.1.1 Linear discriminant analysis
• 12.4.1.2 Bayesian linear regression
• 12.4.1.3 Logistic regression
• 12.4.1.4 Support vector machine
• 12.4.1.5 Other machine-learning algorithms
• 12.4.1.6 Summary
• 12.4.2 Neural networks
• 12.4.2.1 Multilayer perceptron
• 12.4.2.2 Some deep-learning techniques
• 12.4.2.3 Summary
• 12.5 Conclusion
• Acknowledgment
• References

Inspec keywords: brain-computer interfaces; electroencephalography; medical signal processing; feature extraction

Other keywords: classification algorithms; BCI research; rapid serial visual presentation-based brain-computer interface systems; commonly deployed algorithms; feature extraction; image RSVP-based; RSVP protocol; RSVP-EEG development; target-search BCI; chapter

Subjects: Computer vision and image processing techniques; Electrical activity in neurophysiological processes; Biology and medical computing; Optical, image and video signal processing; Electrodiagnostics and other electrical measurement techniques; Bioelectric signals; Signal processing and detection; Digital signal processing