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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Zhao, Meiling Sun, Shufan Wang, Ling Ren, Jing Mo, Weiyi Yang, Jiajia Medical School Tianjin University Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Although different general anesthetics induce similar physiological states, their effects on neuronal activity are quite different, which is closely related to brain function such as memory and cognition. Hippocampus serves a critical function in memory and cognition, where somatostatin-expressing (SST+) interneurons are the main source of inhibitory synapses on excitatory neurons. However, how the activity of this subpopulation of neurons changes in response to different anesthetics has not been clearly explored. Through switching different anesthetic strategies and using chronic in vivo two-photon imaging recordings, we monitored the effects of isoflurane (Iso), medetomidine/midazolam/fentanyl (MMF), and ketamine/xylazine (Keta/Xyl) on the calcium activity of SST+ interneurons in hippocampal CA1 region of adult mice. Compared to the awake state, all three anesthetics greatly reduced the amplitude of calcium activity in SST+ interneurons and induced different activity patterns. The correlation during anesthesia with Iso was most similar to the awake state, whereas MMF or Keta/Xyl resulted in neuronal de-correlation and had different recovery efficiencies. In conclusion, our results suggest that there are unique effects of the three anesthetics on hippocampal calcium dynamics and correlated activity. Our findings may contribute to the evaluation and development of anesthesia strategies in biological research and clinical areas. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Neurons

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The fNIRS-based Emotion Recognition by Spatial Transformer and WGAN Data Augmentation Towards Developing A Novel Affective BCI

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IEEE Transactions on Affective Computing 2024年第2期16卷 875-890页

作者： Si, Xiaopeng Huang, He Yu, Jiayue Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin300072 China Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin300072 China

The affective brain-computer interface (aBCI) facilitates the objective identification or regulation of human emotions. Current aBCI mainly relies on electroencephalography (EEG). However, research shows that emotions involve a large-scale distributed brain network. Compared to electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) offers a higher spatial resolution. It holds greater potential in capturing emotional spatial information, which may foster the development of new affective brain-computer Interfaces (aBCI). We proposed a novel self-attention-based deep-learning transformer language model for fNIRS cross-subject emotion recognition, which could automatically learn the emotion's spatial attention weight information with strong interpretability. Besides, we performed data augmentation by introducing the wasserstein generative adversarial networks (WGAN). Results showed: (1) We achieved 84% three-category cross-subject emotion decoding accuracy. The spatial transformer module and WGAN improved the accuracy by 12.8% and 4.3%, respectively. (2) Compared with cutting-edge fNIRS research, we led by 10% in three-category decoding accuracy. (3) Compared with cutting-edge EEG research, we lead by 28% in arousal decoding accuracy, 10% in valence decoding accuracy, and 2% in three-category decoding accuracy. (4) Besides, our approach holds the potential to uncover the brain's spatial encoding mechanism of human emotion processing, providing a new direction for building interpretable artificial intelligence models. © 2010-2012 IEEE.

关键词： Near infrared spectroscopy

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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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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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Astrocytic TRPA1 as a target in reversing social avoidance by low-intensity focused ultrasound stimulation in mice 46

Astrocytic TRPA1 as a target in reversing social avoidance b...

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

作者： Wei, Ganjiang Wang, Ling Zhu, Jingnan Wang, Xiao Zang, Yuran Yang, Jiajia Zheng, Chenguang 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

Social avoidance is a prominent clinical feature of multiple neuropsychiatric disorders. Increasing evidence showed that the medial prefrontal cortex (mPFC) regulates social avoidance with suppression of excitatory neurons. Our previous study found that the low-intensity focused ultrasound (LIFUS), as a novel noninvasive neuromodulation technique, could alleviate the social avoidance of social defeat stress (SDS) model mice by increasing the mPFC excitation. However, the underlying target of LIFUS on mPFC neurons in social defeated mice remains unclear. Here we injected AAV-shRNA to knock down astrocytic TRPA1 in the mPFC, and investigated whether astrocytic TRPA1 played a crucial role in LIFUS modulation. We found that the social avoidance of SDS mice with TRPA1 knocked down was not affected by LIFUS. Furthermore, LIFUS didn't induce enhanced expression of membrane NMDAR2A and NMDAR2B in SDS mice with TRPA1 knocked down, compared with that in mice infected with the control virus. It is believed that LIFUS can act on astrocytic TRPA1 to improve social avoidance and hence synaptic function. © 2024 IEEE.

关键词： Ultrasonics

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