  In recent years, the rapid development of neuroimaging technology has been
providing many powerful tools for cognitive neuroscience research. Among them,
the functional magnetic resonance imaging (fMRI), which has high spatial
resolution, acceptable temporal resolution, simple calibration, and short
preparation time, has been widely used in brain research. Compared with the
electroencephalogram (EEG), real-time fMRI-based brain computer interface
(rtfMRI-BCI) not only can perform decoding analysis across the whole brain to
control external devices, but also allows a subject to voluntarily
self-regulate specific brain regions. This paper reviews the basic architecture
of rtfMRI-BCI, the emerging machine learning based data analysis approaches
(also known as multi-voxel pattern analysis), and the applications and recent
advances of rtfMRI-BCI.
