This chapter is a comprehensive overview of noninvasive brain-machine interface (BMI). Though intended for nonspecialists, it contains some technical details and the background materials for the rest of the book. After introducing the core components of the BMI systems, this chapter describes various possibilities for brain activity measurements. It then emphasizes on electroencephalogram (EEG), which will be used as the source of the signals for BMI in the rest of the book. Next, possible standard preprocessing algorithms commonly used in EEG-based BMIs are illustrated along with the main categories of features extracted from EEG and used for classifications. Finally, some possible applications of BMI are described.

Chapter Contents:

• Abstract
• 1.1 Introduction
• 1.2 Core components of a BMI system
• 1.3 Signal acquisition
• 1.3.1 Electroencephalography
• 1.3.2 Positron emission tomography
• 1.3.3 Magnetoencephalography
• 1.3.4 Functional magnetic resonance imaging
• 1.3.5 Near-infrared spectroscopy
• 1.3.6 Commonly used method in BMI—why EEG?
• 1.4 Measurement of EEG
• 1.4.1 Principle of EEG
• 1.4.2 How to measure EEG
• 1.4.3 Practical issues
• 1.4.3.1 Electrode positions
• 1.4.3.2 Reference electrode
• 1.4.3.3 Eye artifacts and electrooculogram
• 1.4.3.4 Attachment of electrodes
• 1.4.3.5 Fixing head and chin
• 1.5 Neurophysiological signals in EEG for driving BMIs
• 1.5.1 Evoked potentials
• 1.5.1.1 Steady-state evoked potentials
• 1.5.1.2 P300
• 1.5.2 Spontaneous signals
• 1.5.2.1 Slow cortical potentials
• 1.5.2.2 Event-related desynchronization (ERD) and event-related synchronization (ERS) in sensorimotor rhythms
• 1.6 Commonly used EEG processing methods in BMI
• 1.6.1 Preprocessing
• 1.6.2 Re-referencing
• 1.6.2.1 Channel selection
• 1.6.2.2 Spectral filtering
• 1.6.2.3 Spatial filtering
• 1.6.2.4 Other preprocessing algorithms
• 1.6.3 Feature extraction
• 1.6.3.1 Temporal features
• 1.6.3.2 Frequency domain features
• 1.6.3.3 Time-frequency features
• 1.6.4 Classification
• 1.7 Feedback
• 1.8 BMI applications
• 1.9 Summary
• References

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

Other keywords: background materials; comprehensive overview; electroencephalogram; BMI systems; nonspecialists; noninvasive brain-machine interface; core components; practical issues; technical details; standard preprocessing algorithms; brain activity measurements

Subjects: Electrical activity in neurophysiological processes; Knowledge engineering techniques; Interactive-input devices; Digital signal processing; Biology and medical computing; Electrodiagnostics and other electrical measurement techniques; User interfaces; Bioelectric signals; Filtering methods in signal processing