Signal processing of functional NIRS data acquired during overt speaking

Functional near-infrared spectroscopy (fNIRS) offers an advantage over traditional functional imaging methods [such as functional magnetic resonance imaging (fMRI)] by allowing participants to move and speak relatively freely. However, neuroimaging while actively speaking has proven to be particularly challenging due to the systemic artifacts that tend to be located in the critical brain areas. To overcome these limitations and enhance the utility of fNIRS, we describe methods for investigating cortical activity during spoken language tasks through refinement of deoxyhemoglobin (deoxyHb) signals with principal component analysis (PCA) spatial filtering to remove global components. We studied overt picture naming and compared oxyhemoglobin (oxyHb) and deoxyHb signals with and without global component removal using general linear model approaches. Activity in Broca’s region and supplementary motor cortex was observed only when the filter was applied to the deoxyHb signal and was shown to be spatially comparable to fMRI data acquired using a similar task and to meta-analysis data. oxyHb signals did not yield expected activity in Broca’s region with or without global component removal. This study demonstrates the utility of a PCA spatial filter on the deoxyHb signal in revealing neural activity related to a spoken language task and extends applications of fNIRS to natural and ecologically valid conditions.