OBJECTIVE: Accurate inter-subject pain intensity assessment using EEG remains a major challenge due to substantial inter-subject variability. This study introduces a novel framework that leverages pain-related brain dynamics and transfer learning to enable reliable inter-subject pain intensity classification. METHODS: The proposed method first quantifies pain sensitivity from resting-state EEG to identify source subjects with comparable neural pain signatures. High-activation pain brain sources are subsequently localized and remapped between source and target subjects. A classifier is trained to evaluate transfer suitability across subjects, and balanced distribution adaptation is applied to align brain source features, mitigating inter-subject variability. The adapted model infers pseudo-labels for the target EEG, which guide the pain response extraction. Final classification is determined by selecting the model exhibiting the minimal cross-domain discrepancy between brain source and pain-evoked EEG features. RESULTS: Experimental evaluations on real EEG datasets demonstrate that the proposed method significantly outperforms three existing approaches in inter-subject pain intensity classification. SIGNIFICANCE: The proposed method effectively overcomes the problem of poor reliability in inter-subject pain intensity classification, providing a robust and clinically viable solution.

Deep learning holds significant potential for bearing fault diagnosis; however, its effectiveness is often hindered by the pervasive issue of imbalanced data in industrial settings, where fault events are inherently rare. To address this widespread challenge, we propose the Class-Aware Supervised Contrastive Quadratic Neural Network (CCQNet), a novel framework combining a class-aware supervised contrastive learning scheme with a quadratic neural network backbone. Our approach introduces two key components to tackle data imbalance: a class-weighted contrastive loss and a logit adjusted cross-entropy loss, which work in tandem to ensure the model pays equal attention to both majority and minority classes. Additionally, we enhance feature extraction through a quadratic convolutional residual network, and provide a novel theoretical analysis linking the function of the quadratic neuron to the principle of autocorrelation in signal processing. Comprehensive experiments on both public and proprietary datasets demonstrate that CCQNet substantially outperforms state-of-the-art methods, particularly in scenarios with extreme data imbalance. The source code is publicly available at https://github.com/yuweien1220/CCQNet for evaluation and validation.

Motorimagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the performance of classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the test data during training. This is unrealistic and impractical in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments conducted on a simulative dataset, BCI competition IV 2a, 2b, and OpenBMI datasets, demonstrate the superiority and competitive performance of our proposed framework over other state-of-the-art methods. Specifically, EEG-DG achieves average classification accuracies of 81.79% and 87.12% on datasets IV-2a and IV-2b, respectively, and 78.37% and 76.94% for inter-session and inter-subject evaluations on dataset OpenBMI, which even outperforms some domain adaptation methods.

Accurate and reliable prediction of bearing remaining useful life (RUL) is crucial to the prognostics and health management of rotation machinery. Despite the rapid progress of data-driven methods, the generalizability of data-driven models remains an open issue to be addressed. In this article, we tackle this challenge by resolving the feature misalignment problem that arises in extracting features from the raw vibration signals. Toward this goal, we introduce a logarithmic cumulative transformation (LCT) operator consisting of cumulative, logarithmic, and another cumulative transformation for feature extraction. In addition, we propose a novel method to estimate the reliability associated with each RUL prediction by integrating a linear regression model and an auxiliary exponential model. The linear regression model rectifies bias from neural network's point predictions while the auxiliary exponential model fits the differential slopes of the linear models and generates the upper and lower bounds for building the reliability indicator. The proposed approach comprised of LCT, an attention GRU-based encoder–decoder network, and reliability evaluation is validated on the FEMETO-ST dataset. Computational results demonstrate the superior performance of the proposed approach several other state-of-the-art methods.

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

Abstract Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are based on the real‐value signal model, with the complex‐value signal model rarely considered. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning framework, a hierarchical model with adaptive Laplace priors is proposed in this paper for recovery of complex sparse signals. Moreover, the space alternating approach is integrated into the algorithm to reduce the computational complexity of the proposed method. In experiments, the proposed algorithm is studied for complex Gaussian random dictionaries and different types of complex signals. These experiments show that the proposed algorithm offers better recovery performance for different types of complex signals than state‐of‐the‐art methods.

Mechanical noise is usually a mixture of narrowband and impulsive noise which needs complex active noise control (ANC) algorithms to improve the de-noising performance. But the ANC algorithm with a high computation load will reduce the real-time performance of an ANC system, thus decreasing the attenuation performance and even leading to divergence. To alleviate this contradiction in narrowband ANC systems, a new virtual filtered-x L0 norm discrete Fourier cancellation (FxL0DFC) based hybrid FxNLMS(filtered-x normalized least mean square)-FxDFC framework is proposed to decrease the total computing load and keep good attenuation performance. For a fast-changing noise, the FxNLMS algorithm is employed. The new virtual FxL0DFC algorithm serves to prepare parameters for steady-state, and when this happens, the FxDFC algorithm with the parameters provided by FxL0DFC is applied. Compared to using the FxNLMS algorithm to attenuate narrowband periodical noise, the FxDFC algorithm has nearly the same tracking performance while having a low computational load. As a result, the FxL0DFC-based FxNLMS-FxDFC algorithm leads to a reduction of the total computational load. Moreover, the proposed method performs excellently in terms of tracking in simulations and experiments on actual data, particularly in environments with rapid power changes and impulsive noise.

OBJECTIVE: The latest development in low-cost single-channel Electroencephalography (EEG) devices is gaining widespread attention because it reduces hardware complexity. Discrete wavelet transform (DWT) has been a popular solution to eliminate the blink artifacts in EEG signals. However, the existing DWT-based methods share the same wavelet function among subjects, which ignores the individual difference. To remedy this deficiency, this article proposes a novel approach to eliminate the blink artifacts in single-channel EEG signals. METHODS: Firstly, the forward-backward low-pass filter (FBLPF) and a fixed-length window are used to detect blink artifact intervals. Secondly, the adaptive bi-orthogonal wavelet (ABOW) is constructed based on the most representative blink signal. Thirdly, these detected signals are filtered by ABOW-DWT. The DWT's decomposition depth is automatically chosen by a similarity-based method. RESULTS: Compared to eight state-of-the-art methods, experiments on semi-simulated and real EEG signals demonstrate the proposed method's superiority in removing the blink artifacts with less neural information loss. SIGNIFICANCE: To filter the blink artifacts in single-channel EEG signals, the innovative idea of constructing an adaptive wavelet function based on the signal characteristics rather than using the conventional wavelet is proposed for the first time.

A non‐linear multi‐sensory adaptive noise canceller (ANC, MsANC) with both multi‐primary and multi‐reference channels is proposed for foetal electrocardiogram (FECG) extraction. The primary channels are connected by a linear combiner (LC) whose output serves as a primary signal for the whole MsANC. A finite impulse response (FIR) filter or a non‐linear filter [a Volterra filter, or an FIR filter plus a functional link artificial neural network (FLANN), or an FIR filter plus a generalised FLANN] is placed in each reference channel to approximate the linear and non‐linear mappings between the chest maternal ECG (ANC reference signal) and the abdominal ECG (ANC primary signal). The LC connecting the primary channels is updated by a constrained recursive least square algorithm, while the linear and non‐linear filters placed in reference channels are updated in a least mean square sense. Two real datasets derived from the Physionet non‐invasive FECG database as well as the DaISy database are used to show the effectiveness of the proposed MsANC. Experimental results have revealed that the proposed MsANC results in considerable performance improvement as the number of primary channels is increased in comparison with existing ANCs with a single primary channel.

In this paper, a variable step-size filtered-x LMS (VSS-FXLMS) algorithm is proposed for a typical narrowband active noise control system. The new algorithm converges much faster than the conventional FXLMS algorithm does, and indicates a convergence rate quite similar to that of the filtered-x recursive least square (FXRLS) algorithm in stationary noise environments. It also considerably outperforms these two existing algorithms in nonstationary situations. The proposed algorithm requires some more computations as compared with the FXLMS algorithm; however, its computational complexity is significantly less than that of the FXRLS algorithm. Numerous simulations for stationary and nonstationary scenarios are conducted to demonstrate the superior performance of the proposed VSS-FXLMS algorithm as compared with the FXLMS and the FXRLS algorithms.

When interharmonics exist in power system signals, large errors emerge in traditional time domain reactive power measurement. In this paper, we present a novel time domain integral method with good effect of restraining interharmonics, synchronization error, and white noise, as well as the principle of the selection of the sampling periods when employing this approach. The current signal and phase-shifted voltage signal are reconstructed after the harmonic components of signals are extracted, so that the interharmonics are filtered. The influence of the synchronization error on the measurement is reduced through removing the weight coefficients of the reactive components. In the simulation, we apply several cosine windows to the proposed method and analyze signals containing both harmonics and interharmonics. The results show that, in the presence of interharmonics, synchronization error, and white noise (with a fundamental signal-to-noise ratio of 40 dB) all together, the relative errors are within the magnitude of 10 −4 , which perfectly satisfies the practical requirement.

Online secondary-path modeling of active noise control (ANC) systems may be effectively implemented by injecting an auxiliary white noise whose magnitude is scaled by a function of residual noise signal. In this paper, a filtered-X LMS (FXLMS) based narrowband ANC system is analyzed, whose online secondary-path modeling is based on the use of an auxiliary white noise scaled by one-sample-delayed residual noise signal. Difference equations governing the dynamics of the entire system and closed-form expressions for steady-state mean-square errors (MSE) as well as the residual noise power are derived and discussed in detail. Extensive simulations are conducted to confirm the validity of the analytical findings.