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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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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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Localisation and decoding of acoustoelectric brain imaging based on anisotropic brain models

Localisation and decoding of acoustoelectric brain imaging b...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Xue Wang Chen Zhang Guowei Chen Jia He Mingyu Li Feng He Hao Zhang Minpeng Xu Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China School of Biomedical Engineering and Technology Tianjin Medical University Tianjin China Haihe Laboratory of Brain -Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Acoustoelectric imaging (AEI) combines electroencephalography (EEG) and transcranial focused ultrasound (tFUS) with the advantages of high temporal and spatial resolution, which is expected to provide safe, accurate and non-invasive neurofunctional imaging of the brain. Previous AEI studies were mainly based on isotropic homogeneous media, but the actual skull and brain white matter show anisotropic resistivity distributions, making it difficult for isotropic media to mimic the imaging results of real brain tissue. In this study, a multi-source AEI dipole localisation and decoding platform was established based on a 128-array ultrasound phase-controlled transducer. Using a phantom of imitation brain tissue and an isolated porcine brain tissue as experimental materials, the discharged dipole was raster scanned by focused ultrasound, and the acoustoelectric (AE) signals were decoded and analysed using an envelope decoding algorithm. The results show that the decoding and localisation of AE signals based on the anisotropic phantom was achieved, with a localisation error of less than 0.6 mm, a localisation signal-to-noise ratio (SNR) of at least 18.17 dB, a decoding half-peak width of at least 0.21 mm, and a decoding accuracy of 0.87. This study will further promote the development of AEI in the field of neuroimaging.

关键词： Neuroimaging brain Ultrasonic imaging Imaging Phantoms Anisotropic Media Electroencephalography Decoding Signal to noise ratio

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Eeg-Based Evaluation of Individual Preferences for Diesel Engine Sound Quality

SSRN

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SSRN 2024年

作者： Guo, Mingkun Han, Siyang Liu, Hongxing Bai, Yanru Lin, Jiewei Ni, Guangjian Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Beijing Shijitan Hospital Capital Medical University Beijing China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China State Key Laboratory of Engines Tianjin University Tianjin China

Diesel engines are prevalent in industrial settings and everyday life, but their high compression rates often result in significant noise emissions. Studies have shown that prolonged exposure to such noise can lead to negative emotional states and health issues. Traditional evaluations of sound quality, which typically rely on subjective and objective assessments, often reflect the preferences of the general population and psychoacoustic perceptions, respectively. However, these evaluations fail to account for the physiological processes involved in sound perception adequately. Electroencephalogram (EEG) signals can provide insights into the brain's physiological responses to external stimuli. This paper proposes an EEG-based method for evaluating sound quality and understanding individual sound quality preferences. EEG signals were collected while participants evaluated diesel engine noise samples. The subjective and psychoacoustic annoyance of the noise samples was analyzed, and EEG features were extracted. The correlation between EEG features and subjective and objective annoyance confirms the feasibility of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Eeg-Based Evaluation of Individual Preferences for Diesel Engine Noise Sound Quality

SSRN

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SSRN 2024年

Diesel engines are prevalent in industrial settings and everyday life, but their high compression rates often result in significant noise emissions. Studies have shown that prolonged exposure to such noise can lead to negative emotional states and health issues. Traditional evaluations of sound quality, which typically rely on subjective and objective assessments, often reflect the preferences of the general population and psychoacoustic perceptions, respectively. However, these evaluations fail to account for the physiological processes involved in sound perception adequately. Electroencephalogram (EEG) signals can provide insights into the brain physiological responses to external stimuli. This paper proposes an EEG-based method for evaluating the sound quality of diesel engine noise and understanding individual sound quality preferences. EEG signals were collected while participants evaluated diesel engine noise samples. The subjective and psychoacoustic annoyance of the noise samples was analyzed, and EEG features were extracted. The correlation between EEG features and subjective and objective annoyance confirms the feasibility of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Study on the Temporal Patterns of CA1 Pyramidal Neuron Activity in Mice Hippocampus under Simulated Spaceflight Composite Environment

Study on the Temporal Patterns of CA1 Pyramidal Neuron Activ...

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

作者： Shufan Sun Jing Ren Meiling Zhao Ling Wang Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

With the gradual realization of manned spaceflight goals, the physical and mental health of astronauts has become a core concern. Numerous studies in recent years have indicated that the aerospace special environment (ASE) can lead to alterations in astronaut cognitive function. However, the adaptive changes in neuronal activity underlying these alterations remain unclear. This study utilized animal models and two-photon imaging technology to investigate the adaptive changes in the activity characteristics and synchronicity of pyramidal neurons in the CA1 region of the mice hippocampus under simulated spaceflight composite environment (SSCE). The results indicate that, with prolonged modeling time, the number of active pyramidal neurons in the CA1 region gradually decreases. Significant reductions in both the frequency and amplitude of neuronal calcium transients occur, stabilizing at lower levels. The calcium transient decay constant extends, and significant decorrelation in neuronal calcium activity is observed. Thus, we posit that these changes may constitute crucial factors leading to the alterations in individual cognitive function induced by the aerospace special environment.

关键词： Adaptation models Calcium Neurons Neural networks Mental health Information processing Mice Decorrelation Transient analysis Hippocampus

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Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition

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computers in Biology and Medicine 2024年 181卷 108973-108973页

作者： Si, Xiaopeng Huang, Dong Liang, Zhen Sun, Yulin Huang, He Liu, Qile Yang, Zhuobin Ming, Dong 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 Tianjin University Tianjin300072 China School of Biomedical Engineering Medical School Shenzhen University Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China

Emotion recognition is crucial for human–computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://***/ThreePoundUniverse/MACTN. © 2024 Elsevier Ltd

关键词： Emotion Recognition

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Low intensity focused ultrasound stimulation targeted on M1 ameliorates neuroinflammation in hemi-parkinsonian rats

Low intensity focused ultrasound stimulation targeted on M1 ...

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

作者： Chunchan Li Tianyi Sun Rong Liang Zihui Yang Weifan Yang Wanzhe Li Yiwei Yin Guanglu Zhang Ling Wang Jiajia Yang Feng He Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Objective: Low intensity focused stimulation (LIFUS) has been proved to improve motor function in Parkinson’s disease (PD) animal modules. The aim of this study is to investigate whether LIFUS target on the primary motor cortex (M1) can improve motor deficit in the PD rats. Methods: The PD rat model was induced by injection of 6-hydroxydopamine (6-OHDA) in the medial forebrain bundle (MFB). Two weeks after the injection, LIFUS was used on PD rats for two weeks. Behavioral tests were performed including open field test and rotarod test to examine the motor ability of the rats. The activity of microglia and astrocyte were tested to evaluate the inflammation level in the brain. The tyrosine hydroxylase (TH) staining was done to detect the recovery of dopaminergic (DA) neurons in the substantia nigra (SN) and DA fibers in the striatum (STR). Results: LIFUS treatment decreased the resting time in OFT(p<0.05) and increased the latency to falls in the rotarod test(p<0.05) compared with the untreated PD rats. Moreover, LIFUS reduced the inflammation response reflected in microglia and astrocyte activation. Additionally, TH-immunoreactive fibers increased in the STR after LIFUS. Conclusion: These findings demonstrated that LIPUS targeted on M1 can inhibit neuroinflammation and improve movement disorders of PD rats. Significance: This study provides a new therapeutic strategy for further clinical application in PD.

关键词： Primary motor cortex Ultrasonic imaging Neurons Rats Forebrain Motors brain modeling Optical fiber testing Engineering in medicine and biology Diseases

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Transcriptomic Study of Neural Regulation in the Medial Prefrontal Cortex of Depressive Mice with Low-Intensity Focused Ultrasound

Transcriptomic Study of Neural Regulation in the Medial Pref...

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

作者： Qiuquan Cai Lianghui Meng Ling Wang Jing Ren Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Exploring new effective treatments for depression holds important social significance and clinical value. Low-intensity focused ultrasound stimulation (LIFUS) has been proven to have significant neuroprotective effects on depression. However, the specific genes regulated by LIFUS in depressive mice remain unclear. We established a depression mice model using chronic restraint stress (CRS) and applied LIFUS to the medial prefrontal cortex (mPFC) following CRS. We explored the impact of modeling and treatment on gene expression changes through transcriptomics, revealing that the therapeutic effect of LIFUS may be associated with the improvement of the activity of calcium signaling pathway and synaptic plasticity. The study provides preclinical evidence and a theoretical basis for applying LIFUS for depression treatment.

关键词： Ultrasonic imaging Calcium Transcriptomics Depression brain modeling Mice Regulation Gene expression Synapses Stress

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