AbstractDesigning optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Available resources often dictate the approach researchers use for optode-layout design. Here we compared four approaches that incrementally incorporated subject-specific magnetic resonance imaging (MRI) information while participants performed mental-calculation, mental-rotation and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB), employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional and vascular information of the same subject (fVASC). The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI and fVASC approaches resulted in a similar outcome. We conclude that additional individual MRI data leads to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.
Signal quality index: an algorithm for quantitative assessment of functional near infrared spectroscopy signal quality
