Real-time fMRI-based Brain Computer Interface: A Review
It provides a comprehensive overview for researchers in neuroscience and BCI, but is incremental as a review paper.
This paper reviews real-time fMRI-based brain computer interfaces (rtfMRI-BCI), covering their architecture, machine learning methods like multi-voxel pattern analysis, and applications, highlighting advantages over EEG such as whole-brain decoding and self-regulation of brain regions.
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.