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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Linlin Dong Yufeng Ke Xiaodong Zhu Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Accuracy Space missions Time factors Engineering in medicine and biology

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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

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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Enhancing SSVEP Identification with Less Individual Calibration Data Using Periodically Repeated Component Analysis

TechRxiv

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TechRxiv 2023年

作者： Ke, Yufeng Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin300072 China

— Objective: Spatial filtering and template matching-based methods are commonly used to identify the stimulus frequency from multichannel EEG signals in steady-state visually evoked potentials (SSVEP)-based brain-computer interfaces (BCIs). However, these methods require sufficient calibration data to obtain reliable spatial filters and SSVEP templates, and they underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulus frequency. Methods: In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted using a self-collected 16-target dataset and a public 40-target dataset. Results: The proposed methods show significant improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a 1-s data length and maximum average information transfer rates of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two datasets, respectively. Significance: Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications of SSVEP-BCIs. © 2023, CC BY.

关键词： brain computer interface

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Deep Residual Neural Networks for Spatial EEG Source Imaging

Deep Residual Neural Networks for Spatial EEG Source Imaging

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Training Neuroscience Simulation Supervised learning Imaging Training data Electroencephalography Spatial databases Biological neural networks Residual neural networks

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The Pattern of CA1 c-Fos Expressing Place Cells Correlates with Learning Progression

The Pattern of CA1 c-Fos Expressing Place Cells Correlates w...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ning Wang Anqi Zhang Jiajia Yang Ling Wang Chenguang Zheng Academy of Medical Engineering and Translational Medicine Medical College Tianjin University 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: (数字)9798350371499

ISBN: (纸本)9798350371505

Spatial memory supported by the hippocampus is a critical cognitive function in daily life. It helps us to navigate to salient goal locations. Hippocampal place cells stably represent the spatial location, while c-Fos-expressing cells (engram cells) contain rich information about the environment, contributing to the encoding and retrieving of memories. However, the role of cFos as a gateway to a more profound comprehension of the mechanisms underlying the process of goal-oriented spatial memory remained poorly understood. In this study, we labeled rats’ c-Fos + neurons by Channelrhodopsin-2 during memory encoding of a specific goal location. A subset of place cells whether they expressed c-Fos was identified optogenetically. As the rats learned the new goal locations, the spatial correlation between task periods remained stable for the c-Fos - place cell, while increased for the c-Fos + place cell. This finding shed light on the processing of goal-oriented spatial memories by the hippocampal network involved in c-Fos. C-Fos - place cells consistently maintained a stable representation of the locations, while c-Fos + place cells exhibited significant flexibility of the goal.

关键词： Correlation Navigation Neurons Cells (biology) Logic gates Encoding Optogenetics Engineering in medicine and biology Hippocampus

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Firing Pattern of Cells in the Hippocampus and Basolateral Amygdala during Novelty Recognition

Firing Pattern of Cells in the Hippocampus and Basolateral A...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiaoxin Ren Yimeng Wang Ling Wang Jiajia Yang Chenguang Zheng Academy of Medical Engineering and Translational Medicine Medical College Tianjin University 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: (数字)9798350371499

ISBN: (纸本)9798350371505

Novelty recognition is essential for exploring and collecting unknown but interesting information, in which multiple brain regions related to cognitive memory are involved. Dorsal hippocampus (dHPC) is revealed to be closely associated with spatial memory and basolateral amygdala (BLA) is reported recently to be related to state-dependent encoding of exploratory behavior. However, how the firing pattern of cells in these two brain regions responds to novelty dynamically remains unclear. In this study, we observed that the representations of cells in BLA exhibited shift towards novelty, whereas dHPC cells maintained strong stability after the introduction of novelty, which are both engaged in the execution of novel recognition. Our study may provide new evidence for underlying the internal mechanisms of perception of novelty in the brain.

关键词： brain In vivo Firing Cells (biology) Encoding Pattern recognition Object recognition Engineering in medicine and biology Hippocampus

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

Enhanced Transient Response of Ultrasound Neuromodulation Me...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xin Li Guoying Zhang Chenguang Zheng Yutao Tian Academy of Medical Engineering and Translational Medicine Medical College Tianjin University 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: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Visualization Ultrasonic imaging Local field potentials Modulation Ions brain modeling Complexity theory Neuromodulation Hippocampus Viruses (medical)

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

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

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ganjiang Wei Ling Wang Jingnan Zhu Xiao Wang Yuran Zang Jiajia Yang Chenguang Zheng Academy of Medical Engineering and Translational Medicine Medical College Tianjin University 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: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Ultrasonic imaging Modulation brain modeling Mice Biomembranes Neuromodulation Engineering in medicine and biology Viruses (medical) Stress Immune system

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

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

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Visualization Time-frequency analysis Accuracy Approximation algorithms Real-time systems Electroencephalography Decoding Windows Steady-state Engineering in medicine and biology

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Impairments in Spatial Representations of Place Cells and Neural rhythms In APP knock-in rat model 46

Impairments in Spatial Representations of Place Cells and Ne...

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

作者： Wang, Xueling Wang, Yimeng Pang, Keliang Lu, Bai Tian, Yutao Zheng, Chenguang Fudan University Aging and Longevity Institute Institute of Biological Science Zhongshan Hospital Shanghai China Tsinghua University-Peking University Joint Center for Life Sciences Tsinghua University School of Pharmaceutical Sciences IDG/McGovern Institute for Brain Research Beijing China 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

Alzheimer's disease (AD) is a neurodegenerative disease that severely impacts spatial memory. Place cells in the hippocampus play an important role in spatial memory. However, neuronal activity in the hippocampus is difficult to monitor and study in human AD patients. Thus, it is critical to have an animal model that resembles closely human AD. The newly developed APPNL-G-F rat model, which is a knock-in rat line of the amyloid precursor protein (APP) with Swedish-Beyreuther/Iberian-Arctic mutations, presents a unique opportunity. In this study, we investigated how hippocampal place cells and neural rhythms were impaired in APP rats. Place cells of APP rats showed a less defined spatial tunning and disrupted remapping. In addition, APP rats exhibited decreased power of theta rhythm and slow gamma rhythm. These results suggest that the impairment of spatial memory in APP rats may be caused by the damage of place cells' ability to represent spatial location and abnormal neural rhythm in the hippocampus. © 2024 IEEE.

关键词： Rats

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