  We solve the fNIRS left/right hand force decoding problem using a data-driven
approach by using a convolutional neural network architecture, the HemCNN. We
test HemCNN's decoding capabilities to decode in a streaming way the hand, left
or right, from fNIRS data. HemCNN learned to detect which hand executed a grasp
at a naturalistic hand action speed of $~1\,$Hz, outperforming standard
methods. Since HemCNN does not require baseline correction and the convolution
operation is invariant to time translations, our method can help to unlock
fNIRS for a variety of real-time tasks. Mobile brain imaging and mobile brain
machine interfacing can benefit from this to develop real-world neuroscience
and practical human neural interfacing based on BOLD-like signals for the
evaluation, assistance and rehabilitation of force generation, such as fusion
of fNIRS with EEG signals.
