Acupuncture at pericardium 6 (PC6) has been clinically found to alleviate anxiety disorders and is thought to be related to regulatory effects on the autonomic nervous system, central nervous system, and default mode network. However, the relationship between the temporal characteristics of microstate changes and the related PC6 acupoints with twirling frequency manipulation remains unclear. In this study, resting-state electroencephalogram signals from 10 healthy participants were recorded at the PC6/sham acupoint with strong/weak twirling frequency manipulation. Electroencephalogram microstate analysis was conducted to obtain microstate maps and microstate parameters under different conditions. Our results showed the regulatory effects of PC6 on microstate classes C and D; the occurrence of class C was related to twirling frequency, and the duration of class D was related to acupuncture manipulation. Taken together, our findings indicate that the temporal characteristics of the microstates of classes C and D maybe essential for PC6 acupuncture with twirling frequency manipulation.

Brain-controlled robotic arms (BCRAs) have emerged as promising end-effectors in Internet of Things (IoT) environments, enabling direct neural control of physical interactions in remote and complex scenarios. However, existing BCRA systems face challenges in achieving precise fine-grained control and adapting to diverse manipulation tasks. To address these limitations, this study proposes a novel BCRA system with Human-Centered Visual Evoked Potential (HC-VEP) paradigm. By leveraging vision-based spatial mapping, the system enables fine-grained, coordinate-level manipulation of the BCRA in complex 3D environments. To further enhance system performance, Foveal Attention Tracking (FAT) is integrated to rapidly estimate the user's intended grasp location, thereby improving interaction efficiency. Additionally, a Time-Frequency Domain Enhanced Network (TFDE-Net) is developed to improve electroencephalogram (EEG) decoding accuracy through advanced time-frequency feature extraction. Experimental results demonstrate the effectiveness of the proposed system. Offline evaluations show that TFDE-Net achieves a peak information transfer rate (ITR) of 111.81 bits/min, representing a 20.8% improvement over EEGNet. Online experiments in simulated environments demonstrate the efficiency of our paradigm. Specifically, HC-VEP with FAT reduces task completion time by 56.86% compared to HC-VEP without FAT, and by 68.17% compared to traditional SSVEP. Real-world validation experiments with physical robotic arms achieved an overall success rate of 90.0% in unshielded laboratory environments, demonstrating the system's robustness under realistic operating conditions. These findings validate the system's capability for flexible and efficient grasping of arbitrary objects in complex 3D environments, marking a important step toward in practical BCRA applications.

In noninvasive brain-computer interfaces (BCIs), EEG analysis plays a critical role, with neural networks serving as a cornerstone for signal decoding. Existing neural network approaches for EEG signal recognition require extensive manual design and hyperparameter tuning, leading to inefficiencies and making them impractical for embedded devices due to their large model size. To address these limitations, we propose mixed-level differentiable and hardware-aware neural architecture search (MDH-NAS), a framework that automatically generates lightweight neural networks tailored for EEG classification. Unlike traditional DARTS methods, MDH-NAS employs a hybrid optimization strategy that balances global and local search spaces, thereby accelerating and refining architecture discovery. It introduces explicit size constraints during the search process to ensure deployability on embedded devices. MDH-NAS demonstrates autonomous generation of architectures for tasks such as motor imagery (MI) and depression recognition, achieving 87.80% accuracy on the BCI-IV dataset and 90.09% on the MODMA dataset. When deployed on the EAIDK-610 board across heterogeneous tasks, it attains 85.37% accuracy on the EEG Motor Movement/Imagery dataset. This method reduces architecture discovery time by 89% and enhances prediction accuracy by 8.70% compared to baseline methods, highlighting its potential for scalable EEG analysis and real-world embedded deployment.

Many headache patients often report cognitive disturbances, but tactile cognitive data are limited. Applying computing-aided strategies to reveal the association between migraine without aura (MOA) or tension-type headache (TTH) and tactile cognition is one of the research highlights. The aim of this study was to investigate whether MOA or TTH patients had a decline in tactile discrimination by utilizing a tactile angle discrimination tester. A cross-sectional study was performed between 1 January 2021, and 1 January 2022. A total of 301 participants were enrolled, with 107 in control, 90 in MOA, and 104 in TTH groups. A tactile cognition tester was used to objectively examine tactile discrimination in all participants. Tactile angle discrimination thresholds were measured to compare tactile cognitive functions among three groups. There were no statistically significant differences in their demographic characteristics. Compared to the normal control group, the MOA and TTH groups exhibited significantly higher tactile angle discrimination thresholds (showing decline in tactile discrimination), whereas no significant differences were found between the MOA and TTH groups. Differences in tactile angle discrimination thresholds were observed between young (≤ 44 years old) and middle-aged/elderly (≥ 45 years old) participants in the normal control group but not in the MOA and TTH groups. Moreover, the tactile deficits shown in the MOA or TTH groups were evident only in young participants. This study first demonstrated that patients with MOA or TTH, especially those patients younger than 44 years old, had decreased tactile angle discrimination ability, suggesting decline of tactile cognition.

The rapid development of the COVID-19 pandemic has threatened the lives of people around the world. Many people were caught in anxiety and panic, which also prevents people from fully concentrating on their normal lives. However, the current common neurofeedback therapies used to solve the problem of lack of attention cannot fully deal with the differences in each individual. In addition, direct contact between the patient and the doctor also increases the risk of virus transmission during treatment. This article combines neurofeedback and IoT to establish an adaptive attention adjustment method. IoT connects patients and doctors remotely, reducing the direct contact between them. In order to adapt to individual differences, the feedback indicators of each individual are individually calibrated. In addition, the proportional, integral, and derivative controller was used to adjust the difficulty of the feedback task to adapt to each individual’s self-regulation ability and provide the individual with a higher level of regulation. We also designed adaptive attention adjustment experiments for different individuals. The results show that through adaptive feedback training, the individual’s feedback indicator has dropped by 77.90%, and the individual can adjust his attention state to the individual’s optimal baseline threshold, and the oscillation error gradually reduces to the expected threshold range. This method can cope with the differences between different individuals and provide each individual with the same level of feedback regulation. In the future, this study may provide a general adjuvant treatment for other mental illnesses.

This study aims to establish an adaptive attention regulation method by combining neurofeedback and classical feedback control theory. It combined open-loop control and closed-loop negative feedback to achieve a universal attention regulation model. The EEG data under attention stimulation was collected., and the baseline threshold range was selected using the optimal threshold selection method., and each individual was calibrated individually. The sliding window method is used to evaluate the individual's attention state and make the feedback training scene to follow its changes. The experiment found that through adaptive feedback training, the individual can adjust the attention state to the individual's optimal baseline threshold. This method can cope with the differences between different individuals and provide each individual with the same level of feedback regulation. Similarly, this study may provide a universal treatment method for other mental diseases.