Real-time fMRI-based Brain Computer Interface: A Review

TL;DR

This review discusses the architecture, machine learning analysis methods, and recent advances in real-time fMRI-based brain computer interfaces, highlighting their ability for whole-brain decoding and voluntary self-regulation of brain regions.

Contribution

It provides a comprehensive overview of the current state, methodologies, and applications of rtfMRI-BCI, emphasizing recent technological and analytical developments.

Findings

rtfMRI-BCI enables whole-brain decoding and self-regulation.

Machine learning approaches like multi-voxel pattern analysis are central.

Recent advances improve decoding accuracy and application scope.

Abstract

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.