Calibration of Deep Learning Classification Models in fNIRS

arXiv:2402.15266v1 Announce Type: new
Abstract: 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 …

abstract arxiv bci brain brain activity classification computer cs.lg data deep learning development eess.sp functional interfaces monitoring near researchers significance spectroscopy tool type understanding