Quantitative EEG-Based Brain-Computer Interface

The brain-computer interface (BCI) is a direct (nonmuscular) communication channel between the brain and the external world that makes possible the use of neural prostheses and human augmentation. BCI interprets brain signals, such as neural spikes and cortical and scalp EEGs in an online fashion. In this chapter, BCIs based on two types of oscillatory EEG, the steady-state visual evoked potential from the visual cortex and the sensorimotor rhythm from the sensorimotor cortex, are introduced. Details of their physiological bases, principles of operation, and implementation approaches are provided as well. For both of the BCI systems, the BCI code is embedded in an oscillatory signal, either as its amplitude or its frequency. With the merits of robust signal transmission and easy signal processing, the oscillatory EEG-based BCI shows a promising perspective for real applications as can be seen in the example systems described in this chapter. Some challenging issues in real BCI application, such as subject variability in EEG signals, coadaptation in BCI operation, system calibration, effective coding and decoding schemes, robust signal processing, and feature extraction, are also discussed.