Near-infrared spectroscopy (NIRS) has recently been used to measure human motor-cortical activation, enabling the classification of the content of a sensory-motor event such as whether the left or right hand was used. Here, we advance this NIRS application by demonstrating quantitative estimates of multiple sensory-motor events from single-trial NIRS signals. It is known that different degrees of sensory-motor activation are required to generate various hand/finger force levels. Thus, using a sparse linear regression method, we examined whether the temporal changes in different force levels could be reconstructed from NIRS signals. We measured the relative changes in oxyhemoglobin concentrations in the bilateral sensory-motor cortices while participants performed an isometric finger-pinch force production with their thumb and index finger by repeatedly exerting one of three target forces (25, 50, or 75% of the maximum voluntary contraction) for 12 s. To reconstruct the generated forces, we determined the regression parameters from the training datasets and applied these parameters to new test datasets to validate the parameters in the single-trial reconstruction. The temporal changes in the three different levels of generated forces, as well as the baseline resting state, could be reconstructed, even for the test datasets. The best reconstruction was achieved when using only the selected NIRS channels dominantly located in the contralateral sensory-motor cortex, and with a four second hemodynamic delay. These data demonstrate the potential for reconstructing different levels of external loads (forces) from those of the internal loads (activation) in the human brain using NIRS.
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