Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique for monitoring brain activity. To better understand the brain, researchers often use deep learning to address the classification challenges of fNIRS data. Our study shows that while current networks in fNIRS are highly accurate for predictions within their training distribution, they falter at identifying and excluding abnormal data which is out-of-distribution, affecting their reliability. We propose integrating metric learning and supervised methods into fNIRS research to improve networks capability in identifying and excluding out-of-distribution outliers. This method is simple yet effective. In our experiments, it significantly enhances the performance of various networks in fNIRS, particularly transformer-based one, which shows the great improvement in reliability. We will make our experiment data available on GitHub.

翻译：功能性近红外光谱成像（fNIRS）是一种无创监测脑活动的技术。为更深入理解大脑，研究者常利用深度学习解决fNIRS数据的分类难题。本研究表明，尽管当前fNIRS领域的网络在训练分布内预测中具有高精度，但在识别与排除分布外异常数据方面存在不足，这影响了其可靠性。我们提出将度量学习与监督方法整合至fNIRS研究，以提升网络识别与排除分布外异常值的能力。该方法简约而高效。实验证明，该方法能显著提升fNIRS中多种网络的性能，尤其是基于Transformer的网络，其可靠性得到大幅改善。我们将实验数据在GitHub上开源。