Functional Near-Infrared Spectroscopy (fNIRS) is a convenient brain imaging technology that is adaptable to various complex environments. It can detect the brain's responses to external stimuli across different contexts. However, existing fNIRS processing methods fail to mine the brain's sequential association responses in long-term signals over time. To better capture the hidden spatiotemporal correlations and long-range dependencies inherent in fNIRS signals, we propose fNIRS-TTT, a novel classification architecture leveraging Test-Time Training (TTT). Our framework introduces two core innovations: the Cross-Attention Embedding module (CAE) and the TTT Block. The CAE module combines the wide-kernel Patch Conv (capturing cross-channel spatial patterns) and the Channel Conv (processing temporal features within individual channels) together. Their outputs are fused via Cross-Attention to generate spatiotemporal tokens enriched with multi-scale spatial information for fNIRS. The TTT components dynamically adapt to the sequential nature of fNIRS data, effectively capturing long-range temporal context and mitigating overfitting compared to former models. KFold Cross-Validation (KFold-CV) and Leave-One-Subject Cross-Validation (LOSO-CV) are conducted on three open datasets, which demonstrate the superior performance of the proposed fNIRS-TTT compared to state-of-the-art models.

The data bottleneck has emerged as a fundamental challenge in learning based image restoration methods. Researchers have attempted to generate synthesized training data using paired or unpaired samples to address this challenge. This study proposes SeNM-VAE, a semi-supervised noise modeling method that leverages both paired and un-paired datasets to generate realistic degraded data. Our approach is based on modeling the conditional distribution of degraded and clean images with a specially designed graphical model. Under the variational inference framework, we develop an objective function for handling both paired and unpaired data. We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks. Our approach excels in the quality of synthetic degraded images compared to other unpaired and paired noise modeling methods. Furthermore, our approach demonstrates remarkable performance in downstream image restoration tasks, even with limited paired data. With more paired data, our method achieves the best performance on the SIDD dataset.