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.

翻译：功能性近红外光谱（fNIRS）是一种用于监测脑活动的宝贵非侵入性工具。对与意识活动相关的fNIRS数据进行分类，对于增进对大脑的理解及促进脑机接口（BCI）的发展具有重要意义。由于深度学习具有较强的泛化能力和鲁棒性，许多研究者已转向利用深度学习来应对fNIRS数据固有的分类挑战。在fNIRS应用中，可靠性至关重要，而置信度可靠性的一个数学表述形式即为校准。然而，许多研究者忽视了校准这一关键问题。为弥补这一空白，我们提出将校准引入fNIRS领域，并评估现有模型的可靠性。令人惊讶的是，我们的结果表明许多已有模型的校准性能欠佳。为推进fNIRS领域中校准的发展，我们总结了三项实用技巧。通过此信，我们希望强调校准在fNIRS研究中的关键作用，并主张增强基于深度学习的预测在fNIRS分类任务中的可靠性。我们实验过程中的所有数据均已在GitHub上公开提供。