Transformer-based deep learning model for predicting fNIRS short-channel signals.

ABSTRACT:

Significance

Functional near-infrared spectroscopy (fNIRS) enables portable and noninvasive monitoring of cerebral hemodynamics, but hemodynamic changes originating from extracerebral tissues may influence the signals. To avoid this, short-channel regression (SCR) is widely used, yet physical short-separation detectors are not always available or optimally positioned due to hardware limitations or the experimental setup. In such cases, a virtual, data-driven alternative to physical short-channel detectors may be a viable solution.

Aim

We aimed to (i) develop a transformer-based deep learning model to predict short-separation optical density (OD) signals from long-separation channels and (ii) evaluate whether these virtual signals enable effective SCR.

Approach

We trai

SUBMITTER: Guglielmini S 

PROVIDER: S-EPMC12618017 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature