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

作者： Ren, Xiaoxin Wang, Yimeng Wang, Ling 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

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. © 2024 IEEE.

关键词： Brain

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The Pattern of CA1 c-Fos Expressing Place Cells Correlates with Learning Progression 46

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

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

作者： Wang, Ning Zhang, Anqi Yang, Jiajia Wang, Ling 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

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. © 2024 IEEE.

关键词： Signal encoding

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

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

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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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<0.05). Moreover, we found a significantly negative correlation between inter-subject distances calculated from periodic features in resting-state data and inter-subject pairwise classification accuracy (r=-0.472, p<0.001). This finding suggests a promising approach to leverage resting-state data for selecting source subjects that closely match the target subjects.

关键词： Accuracy Correlation Data collection Brain modeling Electroencephalography Data models Calibration Engineering in medicine and biology Monitoring

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Interpretable SincNet-Based Spatiotemporal Neural Network for Seizure Prediction

Interpretable SincNet-Based Spatiotemporal Neural Network fo...

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

作者： Baolian Shan Haiqing Yu Hanzhe Jiang Yongzhi Huang Minpeng Xu 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

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.

关键词： Band-pass filters Visualization Sensitivity Accuracy Epilepsy Motors Feature extraction Electroencephalography Spatiotemporal phenomena Convolutional neural networks

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

Transcutaneous auricular vagus nerve stimulation does not mo...

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

作者： Jingxin Chen Yufeng Ke Guangjian Ni 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

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.

关键词： Memory management Modulation Psychology Pupils Engineering in medicine and biology

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

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

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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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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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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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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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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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