Real-Time fMRI-Based Brain Computer Interface: A Review

被引：2

作者：

Wang, Yang ^{[1
]}

Wu, Dongrui ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan, Hubei, Peoples R China

来源：

NEURAL INFORMATION PROCESSING (ICONIP 2017), PT II | 2017年 / 10635卷

关键词：

Brain Computer Interface; Functional Magnetic Resonance Imaging; Machine learning; Multi-voxel pattern analysis; RESONANCE-IMAGING FMRI; SELF-REGULATION; REPRESENTATIONS; NEUROFEEDBACK; PATTERNS; COMMUNICATION; ACTIVATION; NAVIGATION; REGIONS; OBJECTS;

D O I：

10.1007/978-3-319-70096-0_85

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：833 / 842

页数：10

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