  Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive tool
for monitoring brain activity. The classification of fNIRS data in relation to
conscious activity holds significance for advancing our understanding of the
brain and facilitating the development of brain-computer interfaces (BCI). Many
researchers have turned to deep learning to tackle the classification
challenges inherent in fNIRS data due to its strong generalization and
robustness. In the application of fNIRS, reliability is really important, and
one mathematical formulation of the reliability of confidence is calibration.
However, many researchers overlook the important issue of calibration. To
address this gap, we propose integrating calibration into fNIRS field and
assess the reliability of existing models. Surprisingly, our results indicate
poor calibration performance in many proposed models. To advance calibration
development in the fNIRS field, we summarize three practical tips. Through this
letter, we hope to emphasize the critical role of calibration in fNIRS research
and argue for enhancing the reliability of deep learning-based predictions in
fNIRS classification tasks. All data from our experimental process are openly
available on GitHub.
