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（fNIRS）是一种用于监测大脑活动的非侵入性技术。为更深入地理解大脑，研究者常借助深度学习解决fNIRS数据的分类难题。本研究表明，当前fNIRS领域的神经网络虽能对训练分布内的预测保持高准确率，但在识别和排除分布外的异常数据方面表现欠佳，进而影响其可靠性。我们提出将度量学习与监督方法整合至fNIRS研究，以提升网络识别并排除分布外异常值的能力。该方法简单而有效。实验中，它显著增强了多种fNIRS网络的性能，尤其是基于Transformer的网络，其可靠性得到大幅提升。我们将于GitHub公开实验数据。