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TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments

Scientific Data 2025年第1期12卷 1-14页

作者： Bai, Yanru Tang, Qi Zhao, Ran Liu, Hongxing Zhang, Shuming Guo, Mingkun Guo, Minghan Wang, Junjie Wang, Changjian Xing, Mu Ni, Guangjian Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin 300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300392 China National University of Defense Technology Hunan Changsha 410000 China

Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400–450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models. © The Author(s) 2025.

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Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task

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Neuromodulation 2025年

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China Department of Biomedical Engineering College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV). Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task. Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks. Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV. Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions. © 2025 International Neuromodulation Society

关键词： Heart rate variability (HRV) LC-NA pupil response pupillometry taVNS

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Improving EEG-Based Cross-Subject Mental Workload Classification Performance with Euclidean-Aligned Periodic and Aperiodic Features 46

Improving EEG-Based Cross-Subject Mental Workload Classifica...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Tao Ke, Yufeng He, Feng Ming, Dong Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Academy of Medical Engineering and Translational Medicine Tianjin300392 China

ISBN: (纸本)9798350371499

Enhancing the cross-subject classification performance of EEG-based mental workload (MWL) monitoring models poses a significant challenge. Traditional methods require gathering calibration data for new users to prevent performance decline. However, the calibration data collection process is time-consuming and labor-intensive. In this study, we proposed a novel cross-subject MWL classification model that does not require calibration data. Specifically, we used periodic and aperiodic components obtained through EEG spectrum decomposition as features, replacing the commonly used power spectral density (PSD) features. These features are then aligned across subjects using a modified Euclidean alignment method. Our results show that the aligned periodic and aperiodic combined features achieve the highest classification accuracy (0.791±0.077), significantly surpassing raw PSD features without alignment (0.731±0.086, p © 2024 IEEE.

关键词： Calibration

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Effects of 15-Day -6° Head-Down Bed Rest and HD-tDCS on Cognitive Functions 46

Effects of 15-Day -6° Head-Down Bed Rest and HD-tDCS on Cog...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Dong, Linlin Ke, Yufeng Zhu, Xiaodong Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin 300392 China

ISBN: (纸本)9798350371499

A favorable cognitive state, efficient attention, and sharp alertness are essential for successful space missions. To explore the effects of microgravity on working memory, attention, and alertness and to find effective approaches to improve them, we recruited 17 healthy participants (control: 9, active: 8) to be involved in a 15-day -6° head-down bed rest (HDBR). A 30 min HD-tDCS was applied to the active group every other day during HDBR. We adopted three types of tasks: N-back, SCWT, and PVT to evaluate the effects, which were completed before, during, and after HDBR. The results showed that: (1) there was no significant difference in accuracy or in response time between the control and the active group. (2) The accuracy of SCWT before HDBR was significantly lower than that during and after HDBR. (3) Regardless of groups, the response time of all tasks showed a gradual decrease during HDBR. For the N-back and PVT tasks, the response time increased significantly in the control group and decreased significantly in the active group after HDBR. Those demonstrated that the working memory, attention, and alertness were elevated during 15-day -6° HDBR. HD-tDCS enhanced readaptation of cognitive functions after HDBR exposure. © 2024 IEEE.

关键词： Microgravity

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A Novel Real-time Algorithm Based on Phase-Locked Data Alignment for Continuously Controlled SSVEP-BCI 46

A Novel Real-time Algorithm Based on Phase-Locked Data Align...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jiang, Hanzhe Xiao, Xiaolin Mei, Jie Xu, Minpeng Wang, Kun Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China

ISBN: (纸本)9798350371499

Objective: brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) show good performance. However, algorithms always decode segments of electroencephalogram (EEG) and can only satisfy discrete output instructions, which limit the real-time continuous control of the BCI system. This article proposes a novel algorithm for SSVEP-BCI that can translate continuous EEG into control commands, achieving real-time monitoring of user intentions. Methods: A phase synchronicity maximum strategy has been employed in this algorithm, which could capture a fixed-duration SSVEP epoch near any given moment, ensuring each trial is aligned with the phase of the potential corresponding template. Then, the algorithm utilized an update strategy of a small-step sliding window to recognize and output commands in approximately real-time. Results: We constructed an SSVEP-BCI system with continuous stimulation and recruited nine subjects. The results showed that the algorithm proposed in this study efficiently decoded continuously evoked SSVEP signals. The BCI's online average accuracy and ITR were 92.03% and 143.38 bits/min, respectively. Significance: The proposed algorithm can decode SSVEP at any time theoretically, which improves command output density as well as maintains high recognition accuracy. This study provides novel methods for real-time control of external devices using SSVEP-BCIs and helps to develop BCIs that are more compatible with human control habits. © 2024 IEEE.

关键词： Electroencephalography

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Deep Residual Neural Networks for Spatial EEG Source Imaging 46

Deep Residual Neural Networks for Spatial EEG Source Imaging

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Shi, Qingyuan Yu, Haiqing Huang, Yongzhi He, Feng Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China

ISBN: (纸本)9798350371499

EEG source imaging is an indispensable tool for non-invasive study of brain function. Existing methods mainly directly deal with the EEG inverse problem by imposing prior constraints. However, different brain activation patterns may produce similar potential distributions on scalp EEG, which makes the inverse solution process ill-posed. In this paper, we proposed a framework called DeepMapper for EEG spatial source imaging. It consisted of two parts: dataset simulation and neural network training. In the dataset simulation section, we dealt with the EEG forward problem by using the 3-shell realistic head model and the boundary element method (BEM). The cortical functional atlas were used to provide physiological constraints. The nonlinear difference equation was used to simulate the brain information. In the neural network training section, we simulated a large amount of EEG-cortex training data. The prior information in weight layers and nonlinear connections was stored using supervised learning. Simulation results showed that DeepMapper's performance was better than traditional methods in recovering EEG spatial sources. Therefore, it suggested that the proposed method may provide a novel approach to EEG source imaging. © 2024 IEEE.

关键词： Inverse problems

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Transcutaneous auricular vagus nerve stimulation does not modulate working memory capacity but alters pupillary responses in the change detection task 46

Transcutaneous auricular vagus nerve stimulation does not mo...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin 300392 China

ISBN: (纸本)9798350371499

Transcutaneous vagus nerve stimulation (taVNS) offers an effective, non-invasive alternative to implantable vagus nerve stimulation and it is considered to be a promising cognitive modulation tool. However, at present, the effect of taVNS on working memory is not clear, Simultaneously, the potential pupillary responses stemming from taVNS require more empirical inquiry. Herein, we investigated the influence of taVNS on working memory capacity and its link to pupillary responses during the change detection task. A sham-controlled, randomized, crossover-designed experiment was applied to 16 participants. Within each session, participants received a Sham or Active taVNS stimulation for 12 minutes and then achieved 3 blocks of change detection task. Each trial exceeds for 5s. The pupil and behavior responses were recorded during the task. We hypothesized that Active taVNS can improve the performance of the change detection task and influence the pupil corresponding to different numbers of target stimuli. For the results, we observed no significant modulation of working memory capacity by taVNS but did note distinct alterations in pupillary response (mean amplitude and extremes) during specified intervals in the change detection task. These findings point to an underexplored avenue of research into the neural or psychological processes affected by taVNS and provides a new insight for taVNS to modulate working memory capacity. © 2024 IEEE.

关键词： pupil response taVNS working memory

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Interpretable SincNet-Based Spatiotemporal Neural Network for Seizure Prediction 46

Interpretable SincNet-Based Spatiotemporal Neural Network fo...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Shan, Baolian Yu, Haiqing Jiang, Hanzhe Huang, Yongzhi Xu, Minpeng Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China

ISBN: (纸本)9798350371499

Spatiotemporal convolutional neural networks (CNNs) have emerged as potent tools for seizure prediction (SP) using electroencephalogram (EEG) signals, probing spatiotemporal biomarkers in epileptic brains. Nevertheless, it poses significant challenges for clinical practice due to the poor interpretability of learned features and the numerous trainable parameters in existing CNNs. To improve the interpretability and performance, this study proposed an interpretable SincNet-based architecture for spatiotemporal CNNs, encompassing EEGNet-8,2, ShallowConvNet, DeepConvNet, and EEGWaveNet, enabling direct visualization of the bandpass temporal filter range using a sinc-convolution layer. Furthermore, we also constructed a visualization analysis method to demonstrate the crucial spatiotemporal features learned by the proposed optimal CNN. Results on the CHB-MIT dataset revealed that both ShallowConvNet and EEGWaveNet had significantly improved performance with more lightweight parameters. Notably, the architecture enabled ShallowConvNet to achieve an average accuracy of 87.2%, sensitivity of 88.3%, weighted F1-score of 87.1%, and AUC of 92.7% for 21 epilepsy patients. Besides, the visualization outcomes underscored the ability of the optimal model to extract statistically significant spatiospectral energy differences within high-frequency EEG bands for SP classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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In Vivo Cortical Microstructure Mapping Using High-Gradient Diffusion MRI Accounting for Intercompartmental Water Exchange Effects

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NeuroImage 2025年 314卷 121258页

作者： Tanxin Dong Hong-Hsi Lee Han Zang Hansol Lee Qiyuan Tian Liang Wan Qiuyun Fan Susie Y Huang Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Interaction Tianjin China. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA. School of Biomedical Engineering Tsinghua University Beijing China. Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China. Haihe Laboratory of Brain-Computer Interaction and Human-Machine Interaction Tianjin China. Electronic address: fanqiuyun@***. Harvard Medical School Boston MA USA Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA USA.

In recent years, mapping tissue microstructure in the cortex using high gradient diffusion MRI has received growing attention. The Soma And Neurite Density Imaging (SANDI) explicitly models the soma compartment in the cortex assuming impermeable membranes. As such, it does not account for diffusion time dependence due to water exchange in the estimated microstructural properties, as neurites in gray matter are much less myelinated than in white matter. In this work, we performed a systematic evaluation of an extended SANDI model for in vivo human cortical microstructural mapping that accounts for water exchange effects between the neurite and extracellular compartments using the anisotropic Kärger model. We refer to this model as in vivo SANDIX, adapting the nomenclature from previous publications. As in the original SANDI model, the soma compartment is modeled as an impermeable sphere due to the much smaller surface-to-volume ratio compared to the neurite compartment. A Monte Carlo simulation study was performed to examine the sensitivity of the in vivo SANDIX model to sphere radii, compartment fractions, and water exchange times. The simulation results indicate that the proposed in vivo SANDIX framework can account for the water exchange effect and provide measures of intra-soma and intra-neurite signal fractions without spurious time-dependence in estimated parameters, whereas the measured water exchange times need to be interpreted with caution. The model was then applied to in vivo diffusion MRI data acquired in 13 healthy adults on the 3-Tesla Connectome MRI scanner equipped with 300 mT/m gradients. The in vivo results exhibited patterns that were consistent with corresponding anatomical characteristics in both cortex and white matter. In particular, the estimated water exchange times in gray and white matter were distinct and differentiated between the two tissue types. Our results show the SANDIX approach applied to high-gradient diffusion MRI data achieves

关键词： Diffusion MRI Kärger model connectome cortex microstructure water exchange

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Enhanced Transient Response of Ultrasound Neuromodulation Mediated by Large Conductance Mechanosensitive Ion Channel 46

Enhanced Transient Response of Ultrasound Neuromodulation Me...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Xin Zhang, Guoying Zheng, Chenguang Tian, Yutao Tianjin University Academy of Medical Engineering and Translational Medicine Medical College Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798350371499

Ultrasound neuromodulation, a burgeoning field aimed at enhancing brain function and behavior, relies on the intricate electrophysiological responses of neurons driven by ion channel dynamics. However, achieving targeted and precise modulation with ultrasound poses a significant challenge. A hypothesis is that mechanosensitive (MS) ion channels may play a pivotal role in achieving the desired specificity in ultrasound effects. Our study involved augmenting MscL-G22S channel expression in specific areas of rats' hippocampus (HP) and primary visual cortex (V1) via AAV virus delivery. We recorded local field potentials (LFPs) to assess the modulation effect and identified distinct evoked-related potentials (ERPs) in regions overexpressing MscL-G22S channels under anesthesia. Notable variations in the amplitude of negative peaks were observed in the ERPs between HP and V1, highlighting a nuanced response to ultrasound stimulation. These results highlight the complexity and promise of MS channel-mediated neuromodulation, offering critical insights into the development of targeted ultrasound interventions to improve brain function. © 2024 IEEE.

关键词： Transient analysis

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