Simple But Effective: Rethinking the Ability of Deep Learning in fNIRS to Exclude Abnormal Input

arXiv:2402.18112v1 Announce Type: cross
Abstract: 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 …

abstract arxiv brain brain activity challenges classification cs.lg current data deep learning eess.sp functional monitoring near networks researchers shows simple spectroscopy study type