  Significance: Optical neuroimaging has become a well-established clinical and
research tool to monitor cortical activations in the human brain. It is notable
that outcomes of functional Near-InfraRed Spectroscopy (fNIRS) studies depend
heavily on the data processing pipeline and classification model employed.
Recently, Deep Learning (DL) methodologies have demonstrated fast and accurate
performances in data processing and classification tasks across many biomedical
fields. Aim: We aim to review the emerging DL applications in fNIRS studies.
Approach: We first introduce some of the commonly used DL techniques. Then the
review summarizes current DL work in some of the most active areas of this
field, including brain-computer interface, neuro-impairment diagnosis, and
neuroscience discovery. Results: Of the 63 papers considered in this review, 32
report a comparative study of deep learning techniques to traditional machine
learning techniques where 26 have been shown outperforming the latter in terms
of classification accuracy. Additionally, 8 studies also utilize deep learning
to reduce the amount of preprocessing typically done with fNIRS data or
increase the amount of data via data augmentation. Conclusions: The application
of DL techniques to fNIRS studies has shown to mitigate many of the hurdles
present in fNIRS studies such as lengthy data preprocessing or small sample
sizes while achieving comparable or improved classification accuracy.
