Abstract
Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity. With advances in signal processing, fMRI data can be analyzed in real time and used in a brain–computer interface (BCI). The feasibility and applications of an fMRI-based BCI (fMRI-BCI) have been studied with increasing frequency in the last decade. This chapter describes aspects of the fMRI-BCI technology and provides a thorough overview of the topic. We review the foundations of the fMRI-BCI, including the fundamental physics and physiology behind fMRI, present a qualitative introduction to relevant statistical analysis techniques, and discuss current applications including control of an external device and therapeutic neurofeedback techniques. Innovations in study design and neuroscience research will increase the suitability for fMRI-BCI to be integrated into clinical methods.
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Acknowledgments
We thank Dr. Liang Guo from the Department of Electrical and Computer Engineering at The Ohio State University, Dr. Julie Golomb from the Department of Psychology at The Ohio State University, Dr. Yi Zhou from the Department of Electrical and Computer Engineering at Duke University, Richard Chan from the Department of Chemistry and Biochemistry at The Ohio State University, and Shwe Han from the Department of Computer Science and Engineering at The Ohio State University for valuable comments and suggestions that helped to improve the manuscript.
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Simon, J., Fishbein, P., Zhu, L., Roberts, M., Martin, I. (2020). Functional Magnetic Resonance Imaging-Based Brain Computer Interfaces. In: Guo, L. (eds) Neural Interface Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-41854-0_2
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