Wenya Nan

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.

Background: Monitoring and evaluating dynamic changes in brain states during electroencephalography (EEG) neurofeedback training (NFT) for post-traumatic stress disorder (PTSD) patients remains challenging when using traditional methods. Method: This study proposes a novel Process Noise Dynamic Adaptation-Mesoscale Mesonetwork Network (PNDA-MMNet) model, which improves upon conventional techniques by establishing a discrete linear dynamic model of the NFT process. The model utilizes a mesoscale intermediate network architecture to create a brain state observation matrix, computes the brain state transition matrix, and applies fuzzy rules for dynamic adaptive noise processing. This maximizes the separability between brain state transitions during NFT and resting states. Results: The proposed model achieves a brain state identification accuracy of 0.7428 ± 0.12 (area under the curve, AUC = 0.84), significantly outperforming conventional algorithms. Interpretations of the model indicate that continuous NFT reduces functional connectivity within the motor cortex, with stronger suppression in the right hemisphere compared to the left. Additionally, it reveals decreased activity in the occipital cortex, particularly in the left occipital region, where inhibition increases radially from the midline. Notably, the connectivity between the motor and occipital cortices remains stable throughout the training process. These connectivity changes reflect NFT-induced modulation of cortical activity and are consistent with known neurophysiological patterns in PTSD, highlighting their potential relevance to therapeutic mechanisms. Conclusion: This research introduces a more effective approach for real-time monitoring and evaluation of PTSD patients’ brain states during NFT, offering a quantitative method for assessing treatment efficacy and guiding therapeutic interventions.

As a significant component of executive function, goal-directed attentional control is crucial for cognitive processing and is closely linked to frontal-midline theta (FMT) rhythms. However, how up-regulation and down-regulation of FMT through neurofeedback training (NFT) impact goal-directed attention control remains unclear, especially for both short-term and long-lasting effects. Therefore, this study employed a single-blind sham-controlled between-subject design to answer this question. Forty-seven healthy adults were randomly assigned to the up-regulation, down-regulation, or sham control groups. Each group underwent one NFT session per day at the Fz electrode site for four consecutive days. All participants completed a visual search task before, immediately after the first, after the final, and one week following the last NFT session. The down-regulation group significantly reduced FMT activity during NFT and in the resting state (p < = 0.038), while the up-regulation group only showed an upward trend during the training phase (r = 0.721, p = 0.002). The behavioral performance showed no significant improvement in any group (p > 0.05). Importantly, the FMT learning efficacy in the up-regulation group revealed a significantly negative correlation with the change in switch cost (r = -0.602, p = 0.046). These findings suggest a close link between the up-regulation efficacy of FMT rhythms and goal-directed attentional control. In educational or clinical settings, it would be desirable to improve goal-directed attention through enhancement of FMT up-regulation efficacy of NFT in future work.

Neurofeedback (NF) training is a closed-loop brain training in which participants learn to regulate their neural activation. NF training of alpha (8–12 Hz) activity has been reported to enhance working memory capacity, but whether it affects the precision in working memory has not yet been explored. Moreover, whether NF training distinctively influences performance in different types of working memory tasks remains unclear. Therefore, the present study conducted a randomized, single-blind, sham-controlled experiment to investigate how alpha NF training affected the capacity and precision of working memory, as well as the related neural change. Forty participants were randomly and equally assigned to the NF group and the sham control group. Both groups received NF training (about 30 min daily) for five consecutive days. The NF group received alpha (8–12 Hz) training, while the sham control group received sham NF training. We found a significant alpha increase within sessions but no significant difference across sessions. However, the behavioral performance and neural activity in the modified Sternberg task did not show significant change after alpha NF training. On the contrary, the alpha NF training group significantly increased visual working memory capacity measured by the Corsi-block tapping task and improved visual working memory precision in the interference condition in a color-recall task. These results suggest that alpha NF training influences performance in working memory tasks involved in the visuospatial sketchpad. Notably, we demonstrated that alpha NF training improves the quantity and quality of visual working memory.

Objectives: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial–temporal (EEG ST) feature learning to automatic emotion classification. Methods: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG’s spatial and temporal features, which are subsequently classified using Softmax. Results: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal–valence, arousal, and valence dimensions, respectively, for the DEAP dataset. Conclusions: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies. Significance: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.