The non-invasive measurement of cerebral functional haemodynamics using near-infrared spectroscopy (NIRS) instruments is often affected by physiological interference. The suppression of this interference is crucial for reliable recovery of brain activity measurements because it can significantly affect the signal quality. In this study, we present a recursive least-squares (RLS) algorithm for adaptive filtering to reduce the magnitude of the physiological interference component. To evaluate it, we implemented Monte Carlo simulations based on a five-layer slab model of a human adult head with a multidistance source-detector arrangement, of a short pair and a long pair, for NIRS measurement. We derived measurements by adopting different interoptode distances, which is relevant to the process of optimizing the NIRS probe configuration. Both RLS and least mean squares (LMS) algorithms were used to attempt the removal of physiological interference. The results suggest that the RLS algorithm is more capable of minimizing the effect of physiological interference due to its advantages of faster convergence and smaller mean squared error (MSE). The influence of superficial layer thickness on the performance of the RLS algorithm was also investigated. We found that the near-detector position is an important variable in minimizing the MSE and a short source-detector separation less than 9 mm is robust to superficial layer thickness variation.

Surface EEG (Electroencephalography) signal is vulnerable to interference due to its characteristics and sampling methods. So it is of great importance to evaluate the collected EEG signal prior to use. Traditional methods usually use the impedance between skin and electrode to estimate the quality of the EEG signal, which has shortcomings such as monotonous features, high false positive rates, and poor real-time capability. Aiming at addressing these issues, this paper presents a novel model of EEG quality assessment based on Fuzzy Comprehensive Evaluation method. The developed model employs amplitude, power frequency ratio, and alpha band PSD (Power Spectral Density) ratio of resting EEG signal as evaluation factors, and performs a quantitative assessment of the signal quality. Experiments show that the proposed model can significantly determine the EEG signal quality. In addition, the model is simple in implementation with low computational complexity, and is able to present the EEG quality evaluation results in real time. Before the formal measurement, collecting short-term resting EEG data, and evaluating the EEG signal quality and current signal acquisition environment using the model, the collection efficiency of qualified EEG signals can be greatly improved.

Near-infrared spectroscopy (NIRS) can be used as the basis of non-invasive neuroimaging that may allow the measurement of haemodynamic changes in the human brain evoked by applied stimuli. Since this technique is very sensitive, physiological interference arising from the cardiac cycle and breathing can significantly affect the signal quality. Such interference is difficult to remove by conventional techniques because it occurs not only in the extracerebral layer but also in the brain tissue itself. Previous work on this problem employing temporal filtering, spatial filtering, and adaptive filtering have exhibited good performance for recovering brain activity data in evoked response studies. However, in this study, we present a time-frequency adaptive method for physiological interference reduction based on the combination of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). Monte Carlo simulations based on a five-layered slab model of a human adult head were implemented to evaluate our methodology. We applied an EMD algorithm to decompose the NIRS time series derived from Monte Carlo simulations into a series of intrinsic mode functions (IMFs). In order to identify the IMFs associated with symmetric interference, the extracted components were then Hilbert transformed from which the instantaneous frequencies could be acquired. By reconstructing the NIRS signal by properly selecting IMFs, we determined that the evoked brain response is effectively filtered out with even higher signal-to-noise ratio (SNR). The results obtained demonstrated that EMD, combined with HSA, can effectively separate, identify and remove the contamination from the evoked brain response obtained with NIRS using a simple single source–detector pair.

Infrared small target detection (IRSTD) methods have been extensively researched for various military and civilian applications and have greatly developed with the progress of deep learning in recent years. However, the performance of IRSTD remains limited due to challenges such as weak detection capabilities for diverse target boundaries and the complex background clutter present in infrared images across different scenarios. To overcome these challenges, this paper proposes a two-stage end-to-end full-scale dynamic pyramid fusion network (FDPF-Net). This network aims to refine small target boundary information and enhance both background consistency and the contrast between the target and its surroundings. The FDPF-Net introduces a feature extraction trunk sub-network and a full-scale dynamic refinement sub-network to extract and refine target and background information. Additionally, the proposed cross-layer scale adaptive module which is positioned between the trunk and the refinement sub-networks, adaptively integrates and optimizes the full-scale feature representation for boundary feature compensation. Finally, a feature pyramid fusion module is used to fuse and exploit the intrinsic information of small targets, avoiding feature dilution during the information passing process. Experimental results on three public datasets demonstrate that the proposed FDPF-Net outperforms other state-of-the-art methods in terms of Intersection over Union (IoU), Dice Similarity Coefficient (DSC), Precision (Pre), and Sensitivity (Se) and also exhibits more robust segmentation performance. It also maintains a balance between segmentation performance and model complexity, indicating its significant potential for real-world IRSTD applications.

Infrared Small Target Detection (IRSTD) methods have been extensively investigated within the infrared search and tracking applications. U -shaped networks and their improved versions have significantly enhanced IRSTD segmentation performance in recent years. However, existing methods overlook the feature dilution and insufficient representation of long-range dependencies in feature maps, hindering the accurate segmentation of small targets obscured by background clutter. To handle this problem, we designed a multi-scale feature fusion and nested structure-based High-order UNet (MNHU). Our approach utilizes a high-order UNet paradigm that progressively calibrates infrared feature maps, and integrates hierarchical features to enhance both small target and background texture extraction performance. High-order U-Net selectively integrates feature maps from highly correlated adjacent nodes. Subsequently, these encoding or decoding feature maps are progressively merged into a high-order fusion feature map, ensuring sufficient feature extraction and precise feature representation. We evaluate the proposed high-order UNet-based methods (MNHU-E, MNHU-D, and MNHU) on three public datasets. The results underscore our method's superior performance in target enhancement and texture awareness, outperforming state-of-the-art techniques in Intersection over Union (IoU), Dice Similarity Coefficient (DSC), Precision (Pre), and Sensitivity (Se). MNHU exhibits robust segmentation capabilities and generalization, effectively achieving a balance between model complexity and performance, which highlights its suitability for practical applications. (The code is available at https://github.com/Y-xiaoyang/MNHU-Net.)