Electroencephalogram (EEG) signals are inherently non-stationary and exhibit significant inter-subject variability, leading to pronounced cross-subject distribution shifts that hinder accurate emotion recognition. Although graph convolutional networks (GCNs) and domain adaptation (DA) methods have made progress in mitigating individual differences, existing approaches still face two fundamental limitations: (1) traditional GCNs rely on static functional connectivity graphs, which fail to capture the dynamic temporal evolution of neural interactions during emotional processes, and (2) most DA-based methods only emphasize global feature alignment while overlooking emotion-specific semantic structures, thereby impairing both fine-grained discriminability and cross-subject generalization. To overcome these challenges, we propose the Prototypical Contrastive Learning with Temporal Dynamic Graph Convolutional Network (PCL-TDGCN) for EEG-based emotion recognition. Specifically, we construct an adaptive global EEG pattern memory mechanism to model temporally dynamic brain networks, thereby facilitating spatiotemporal neural interactions essential for emotion representation learning. Furthermore, we design a prototypical contrastive learning strategy that incorporates: (i) intra-domain contrastive learning to enhance the discriminability of emotional state representations, and (ii) inter-domain contrastive learning to mitigate distribution shifts across domains via semantic-aware prototypical alignment. Extensive experiments on three public datasets demonstrate that the proposed PCL-TDGCN outperforms state-of-the-art methods, achieving accuracy improvements of 1.08% (SEED), 6.53% (HIED), and 0.98% (SEED-IV) in subject-dependent experiments, and 1.08% (SEED), 7.51% (HIED), and 1.99% (SEED-IV) in subject-independent scenarios, respectively.

Effective regulation of social decision-making is crucial for achieving equitable outcomes in human interactions. This study explores the impact of endogenous regulation on social decision-making and associated neural changes through a neurofeedback (NF) training framework. Given the relationship between social decision making, emotions, and frontal alpha asymmetry (FAA), this NF training enables individuals to self-regulate their FAA, thereby influencing their decision-making behavior. Eighty-one participants were randomly divided into the up-FAA group aiming at up-regulating FAA, the down-FAA group aiming at down-regulating FAA, and the sham-NF group. First, our results validated the specific NF training effect on selfregulating FAA. Notably, not all participants in the up-FAA and down-FAA groups successfully learned to regulate their FAA, leading to further subdivision into up-learner, down-learner, up-nonlearner, and down-nonlearner categories based on learning efficacy. Participants who effectively learned to reduce their FAA (down-learners) showed significant changes in decision behavior under moderately unfair conditions, characterized by increased rejection rates during the ultimatum game (UG) task. They also exhibited larger N200 amplitudes while balancing the decisionmaking period. In contrast, up learners demonstrated minimal behavioral changes despite increases in FAA. We conclude that decreases in FAA have a more pronounced impact on social decision-making than increases during NF training. This study highlights the effects of FAA self-regulation on fairness-related decision-making, revealing the neurobiological factors that shape decisions influenced by fairness perceptions. These findings offer valuable insights for enhancing social cooperation and justice.

The interaction of the brain's decision-making and feedback stages is crucial for guiding human behavior. Previous studies mainly focused on the interaction immediately after the feedback, resulting in a limited understanding of brain communication dynamics during the interaction process. This study examined the communication dynamics of the brain network during decision-feedback interaction under various feedback conditions by employing a newly developed activation network approach to reveal its underlying neural mechanism. Thirty participants completed a decision-feedback task that involved a sequence of cue-induced predictions with highly predictable, somewhat predictable, and unpredictable feedback conditions. We constructed the activation network for all experimental stages using source-level EEG data in the alpha band. Notably, the brain exhibited the highest communication efficiency ($p < 0.05$) in receiving and integrating feedback with decision-making information during the feedback stage. Furthermore, the network-behavior correlations indicated that the brain tends to evaluate unexpected feedback under highly predictable conditions and expected feedback under unpredictable conditions, suggesting distinct neural strategies of the decision-feedback interaction process. Finally, we decoded the optimization process of decision-feedback interaction across the entire task. Although network correlations between the decision and feedback stages decreased over time (high predictable: $r = -0.447$, $p = 0.001$; unpredictable: $r = -0.305$, $p = 0.032$), classification accuracy significantly improved (${r = -0.448}$, $p = 0.010$, best accuracy: 86.667% ) under the highly predictable condition, corresponding with enhanced prediction behavior. These results indicate the optimization process of the cognitive resources allocation that supports more efficient interaction and improved predictive performance. Our findings advance the understanding of the mechanisms of decision-feedback interaction.

Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) hold significant promise for enabling high-speed human-computer interaction in real-world scenarios. However, existing frequency-domain decoding methods treat frequency spectrum features (the real and imaginary spectrum features) as a single feature without considering their unique spatial and spectral characteristics, resulting in insufficient generalizable features and limited classification accuracy in cross-subject scenarios. To address this issue, we propose a Dual-Branch Attention-Based Frequency Domain Network (DB-AFDNet) to independently decode real and imaginary spectral components, aiming to acquire more discriminative and generalizable features for cross-subject applications. Specifically, we construct inter-branch attention similarity constraints to encourage the two branches to have similar attention properties, promoting to learn the consensus characteristics in the dual branches. Furthermore, we propose intra-branch orthogonality constraints to explore branch-specific discriminative features to learn generalizable features. Experimental studies on two public datasets, the Benchmark and Beta datasets, demonstrate that DB-AFDNet outperforms state-of-the-art methods in cross-subject classification, achieving a relative improvement of 1.36$\%$ and 1.45$\%$, respectively.

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://github.com/YYingDL/NSAL-DGAT.

In electroencephalographic-based (EEG-based) emotion recognition, high non-stationarity and individual differences in EEG signals could lead to significant discrepancies between sessions/subjects, making generalization to a new session/subject very difficult. Most existing domain adaptation (DA) and multi-source domain adaptation (MSDA) techniques aim to mitigate this discrepancy by aligning feature distributions. However, when confronted with many diverse domain distributions, learning domain-invariant features via aligning pairwise feature distributions between domains can be hard or even counterproductive. To address this issue, this article proposes an attention alignment approach to learning abundant domain-invariant features. The motivation is simple: despite individual differences causing significant differences in feature distributions in EEG-based emotion recognition, shared affective cognitive attributes (attention) of spectral and spatial domains can be observed within the same emotion categories. The proposed spectral-spatial attention alignment multi-source domain adaptation (S2A2-MSDA) constructs domain attention to represent affective cognition attributes in spatial and spectral domains and utilizes domain consistent loss to align them between domains. Furthermore, to facilitate discriminative feature learning on the target classes, S2A2-MSDA learns the conditional semantic information of the target domain using a pseudo-labeling method. This algorithm has been validated on the SEED and SEED-IV datasets in cross-session and cross-subject scenarios, respectively. Experimental results demonstrate that S2A2-MSDA outperforms existing representative DA and MSDA methods, achieving state-of-the-art performance.

With smart portable devices such as smartphones and tablets in usage and popularity, people are more willing to use these devices to scan and save digitized documents. However, when capturing document images, shadows are inevitable and influence clarity and readability. How to remove the shadows of document images is an important and meaningful task. In this paper, we propose a water-filling method using chroma adjustment for shadow removal. Firstly, a global and local jointly water-filling approach is designed to estimate the shading map. Then, we design an adaptive global brightness adjustment strategy to optimize the global luminance of the output image. Since only adjusting brightness can cause color distortion of output images, we propose an adaptive chroma adjustment strategy to ensure color consistency across all areas of output images. A series of experiments show that our method can remove shadows of digitized documents, outperforming some state-of the-art methods. Moreover, the proposed method can keep the brightness and color as consistent as possible with the non-shadow area.

Learning from subject's calibration data can significantly improve the performance of a steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI), for example, the state-of-the-art target recognition methods utilize the learned subject-specific and stimulus-specific model parameters. Unfortunately, when dealing with new stimuli or new subjects, new calibration data must be acquired, thus requiring laborious calibration sessions, which becomes a major challenge in developing high-performance BCIs for real-life applications. This study investigates the feasibility of transferring the model parameters (i.e., the spatial filters and the SSVEP templates) across two different groups of visual stimuli in SSVEP-based BCIs. According to our exploration, we can extract a common spatial filter from the spatial filters across different stimulus frequencies and a common impulse response from the SSVEP templates across different neighboring stimulus frequencies, in which the common spatial filter is considered as the transferred spatial filter and the common impulse response is utilized to reconstruct the transferred SSVEP template according to the theory that an SSVEP is a superposition of the impulse responses. Then, we develop a transfer learning canonical correlation analysis (tlCCA) incorporating the transferred model parameters. For evaluation, we compare the recognition performance of the calibration-free, the calibration-based, and the proposed tlCCA on an SSVEP data set with 60 subjects. Experiment results prove that the spatial filters share commonality across different frequencies and the impulse responses share commonality across neighboring frequencies. More importantly, the tlCCA performs significantly better than the calibration-free algorithms, comparably to the calibration-based algorithm. Note to Practitioners-This work is motivated by the long calibration time problem in using an steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) because most state-of-the-art frequency recognition methods consider merely the situation that the calibration data and the test data are from the same subject and the same visual stimulus. This article assumes that the model parameters share the stimulus-nonspecific knowledge in a limited stimulus frequency range, and thus, the subject's old calibration data can be reused to learn new model parameters for new visual stimuli. First, the model parameters can be decomposed into the stimulus-nonspecific knowledge (or subject-specific knowledge) and stimulus-specific knowledge. Second, the new model parameters can be generated via transferring the knowledge across stimulus frequencies. Then, a new recognition algorithm is developed using the transferred model parameters. Experiment results validate the assumptions, and moreover, the proposed scheme could be extended to other scenarios, such as when facing new subjects, or adopting new signal acquisition equipment, which would be helpful to the future development of zero-calibration SSVEP-based BCIs for real-life healthcare applications.

Data-driven classification approaches have substantially boosted the classification performance in steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI). However, as a tradeoff to classification accuracy, a long calibration session is required to collect training data, which greatly reduces the applicability of BCI. In order to minimize the calibration effort while retaining good performance, this paper considers the problem of transferring knowledge from historical subjects to new subject, i.e., subject-independent SSVEP-based BCI. To tackle the problem, we propose a novel way to learn the transferable spatial filters by estimating the invariant task-related spatial filter subspace. The bases of the invariant subspace, which we call prototype spatial filters, are robust estimation of the task-related spatial filters. They can be generalized to the unseen subject for better recovering the latent signals. A new classification approach based on the prototype filters, namely transfer template and filter canonical correlation analysis (ttf-CCA), is then proposed and compared with the state-of-art approaches on the SSVEP benchmark data set. The feasibility of the proposed method is validated by the significant improvement on the classification accuracy and information transfer rate (ITR).

The adaptive Fourier decomposition (AFD) is a greedy iterative signal decomposition algorithm in the viewpoint of energy. Instead of using a fixed basis for decomposition, AFD uses an adaptive basis to achieve efficient energy extraction. In the conventional searching method, a new basis is searched from a large dictionary at every decomposition level. This usually results in a slow searching speed. To improve the efficiency, a fast searching method based on Nelder-Mead algorithm is proposed in this paper. The AFD with the proposed searching method is applied for electrocardiography (ECG) signals in which the selection ranges of four key parameters in the proposed searching method are determined based on simulation results of an artificial ECG signal. The simulation results of real ECG data shows that the computational time of the AFD based on the proposed searching method is just half of that based on the conventional searching method with similar reconstruction error.

Among the EEG-based BCIs, SSVEP-based BCIs have gained much attention due to the advantages of relatively high information transfer rate (ITR) and short calibration time. Although in SSVEP-based BCIs the frequency recognition methods using multiple channels EEG signals may provide better accuracy, using single channel would be preferable in a practical scenario since it can make the system simple and easy-to-use. To this goal, we propose a new single channel method based on wavelet-independent component analysis (WICA) in the SSVEP-based BCI, in which wavelet transform (WT) is applied to decompose a single channel signal into several wavelet components and then independent component analysis (ICA) is applied to separate the independent sources from the wavelet components. Experimental results show that most of the time the recognition accuracy of the proposed single channel method is higher than the conventional single channel method, power spectrum (PS) method.

Electroencephalogram (EEG) signals are often contaminated with various artifacts, especially electrooculogram (EOG) or ocular artifacts that cannot be avoided consciously and largely degrade the clinical interpretation of the signals. This paper presents a study on adaptive noise cancellation (ANC) based on adaputive neuro-fuzzy inference system (ANFIS) for EOG artifacts removal, especially when time delay is significant and on real contaminated EEG signal The performance is first evaluated using simulated EEG and EOG signals, further investigation on the effect of time delay and tests on real data are also performed. The results illustrate that ANFIS provides a promising approach to ocular artifact removal with the best performance in comparison with ANC using adaptive filtering andADALINE.

The reduction of the muscle and electrode motion artifacts in ECG using the adaptive Fourier decomposition (AFD) is investigated. This is an extension of our previous work, in which AFD is first proposed for ECG denoising and its effectiveness in filtering out the additive Gaussian white noise is tested. This paper studies the AFD-based ECG denoising method for two types of ECG noise due to the electrode movement and the muscle contraction which are common and important in practice. In addition, some rules on the selection and adjustment of the AFD decomposition level are proposed. The tests on the MIT-BIH Arrhythmia Database indicate that this AFD-based denoising scheme performs better than the Butterworth lowpass filter, the wavelet transform and the empirical mode decomposition methods for ECG denoising with the muscle movement and electrode motion artifacts.