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

作者： Shuguan Cheng Mengmeng Li Haifei Yu Kun Zhao Shuo Liu Hong Wan School of Electrical Engineering Zhengzhou University and Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

It is a hot research direction to reveal the working mechanism of brain by measuring the connection characteristics of brain function network. In this paper, to decode pigeon behavior outcomes in goal-directed decision task, an experiment based on plus maze was designed and the nidopallium caudolaterale (NCL) of the pigeon was selected as the target brain region. The local field potential (LFP) signals in the waiting area (WA) and turning area (TA) were recorded when the pigeons performed the goal-directed tasks. Then, the brain functional connection networks of the LFPs were constructed and the extracted features were applied to decode pigeon behavior outcomes. Firstly, continuous wavelet transform (CWT) was used to carried out time-frequency analysis and the task-related frequency band (40-60 Hz) was extracted. Then, weighted sparse representation (WSR) method was used to construct the functional connectivity network and the related network features were selected. Finally, k-nearest neighbor (kNN) algorithm was used to decode behavior outcomes. The results show that the energy difference between TA and WA in 40-60 Hz band is significantly higher than those in other bands. The selected features have good discriminability for the representation of the differences between WA and TA. The decoding results also suggest the classification performance of the different behavior outcomes. These results show the effectiveness of the WSR to construct the function network to decode behavior outcomes.

关键词： Feature extraction Task analysis Decoding Time-frequency analysis brain modeling Continuous wavelet transforms Electrodes

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Extraction and analysis of abnormal EEG features in children with amblyopia

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Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2025年 175卷 2110765页

作者： Xiaoke Niu Jinxiong Zhang Yanyan Peng Ying Kong Yadong Li Yonghao Han Li Shi Guangying Zheng Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China. Department of Ophthalmology the First Affiliated Hospital Zhengzhou University Zhengzhou 450052 China. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China Department of Automation Tsinghua University Beijing 100084 China. Electronic address: shilits@***. Department of Ophthalmology the First Affiliated Hospital Zhengzhou University Zhengzhou 450052 China. Electronic address: zzzgy@***.

OBJECTIVE:Early and accurate diagnosis of amblyopia is crucial for the healthy development of children. Existing clinical diagnostic methods rely on patient cooperation, which can easily lead to misdiagnosis. The commonly used features derived from visual evoked potentials (VEP) only provided limited information for characterizing the whole brain, highlighting the need for integrating additional data sources, such as brain network metrics, to achieve a more comprehensive understanding. METHODS:We extracted 488 features from 64-channel EEG data recorded from thirty amblyopic children. The features mainly derived from a weighted functional brain network based on coherence across different frequency bands. Feature selection and linear classification techniques were employed to assess their effectiveness in distinguishing amblyopia from normal children. RESULTS:Abnormal EEG features were distributed not only in the occipital lobe but also in non-visual regions, with a higher prevalence in the alpha and beta bands. Their decoding performance surpassed traditional VEP features, and their combination achieved the highest accuracy (89.00%). Moreover, features beyond the occipital lobe exhibited limited decoding performance when considered individually, yet they still have an obvious contribution. CONCLUSIONS:The study identified novel abnormal EEG features associated with amblyopia and demonstrated the potential of multi-channel EEG recordings to assist in the diagnosis of amblyopia. SIGNIFICANCE:The study suggests amblyopia may impair more abilities beyond visual cognition and further provides objective biomarkers for diagnosing amblyopia, which is essential for effective treatment.

关键词： Amblyopic children Decoding EEG Functional connection Weighted brain networks

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Development of wearable miniature neural signal recording system for birds

Development of wearable miniature neural signal recording sy...

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Chinese Automation Congress (CAC)

作者： Liaofeng Wang Lifang Yang Mengmeng Li Zhongliang Yang Zhigang Shang School of Electrical Engineering Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Birds have a wide range of activities in the natural environment, and there are a lot of uncontrollable factors. Therefore, recording the neural signals and behavior data of birds in natural state transition is a major difficulty in the progress of neuroscience. At present, most neuroscience fields usually use wired EEG acquisition equipment, but due to its large size and heavy weight, it is not suitable for small animals, especially birds, to record neural and behavior data in natural state. In order to solve the above problems, we designed a wearable neuro-signal recording system in this study, which can synchronously record the neuro-signal and attitude data of birds in flight. The core processor of this system uses STM32 series high performance single-chip computer, and its peripheral components are described as follows: (1) 8-channel neural signal acquisition module based on AD1299; (2) GPS global positioning module; (3) Attitude measurement unit; (4) TF card storage module. The total weight of the system is about 20 g and the volume is about 27 mm x 25 mm x 15 mm. Finally, the neural signals and attitude data of the pigeons moving freely on the ground and in the sky were collected using the system we designed. The results showed that the system can not only stably record neural signals, attitude information and position information at the same time, but also does not affect their normal flight. The system has a good application prospect. Researchers can use it to record and analyze the neural signals of experimental animals, especially birds in free movement and even flight. Our work provides a portable and feasible experimental tool for further study of the neural mechanism of the brain in more complex environment.

关键词： Birds Global Positioning System Wireless communication Trajectory Memory management Measurement units Acceleration

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Dynamic and low-dimensional modeling of brain functional connectivity on Riemannian manifolds

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NeuroImage 2025年 314卷 121243-121243页

作者： Wang, Mingyu Wang, Yueming Yang, Yuxiao MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China School of Computer Science and Technology Zhejiang University Hangzhou China State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China

Modeling brain functional connectivity (FC) is key in investigating brain functions and dysfunctions. FC is typically quantified by symmetric positive definite (SPD) matrices that are located on a Riemannian manifold rather than the regular Euclidean space, whose modeling faces three challenges. First, FC can be time-varying and the temporal dynamics of FC matrix time-series need to be modeled within the constraint of the SPD Riemannian manifold geometry, which remains elusive. Second, the FC matrix time-series exhibits considerable stochasticity, whose probability distribution is difficult to model on the Riemannian manifold. Third, FC matrices are high-dimensional and dimensionality reduction methods for SPD matrix time-series are still lacking. Here, we develop a Riemannian state-space modeling framework to simultaneously address the challenges. First, we construct a new Riemannian state-space model (RSSM) to define a hidden SPD matrix state to achieve dynamic, stochastic, and low-dimensional modeling of FC matrix time-series on the SPD Riemannian manifold. Second, we develop a new Riemannian Particle Filter (RPF) algorithm to estimate the hidden low-dimensional SPD matrix state and predict the FC matrix time-series. Third, we develop a new Riemannian Expectation Maximization (REM) algorithm to fit the RSSM parameters. We evaluate the proposed RSSM, RPF, and REM using simulation and real-world EEG datasets, demonstrating that the RSSM enables accurate prediction of the EEG FC time-series and classification of emotional states, outperforming traditional Euclidean methods. Our results have implications for modeling brain FC on the SPD Riemannian manifold to study various brain functions and dysfunctions. © 2025 The Authors

关键词： brain functional connectivity Dimensionality reduction EEG Riemannian manifold Stochastic matrix dynamics

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Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games

arXiv

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arXiv 2022年

作者： Bai, Hui Shen, Ruimin Lin, Yue Xu, Botian Cheng, Ran The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The NetEase Games Ai Lab Guangzhou510653 China

Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes non-trivial difficulties for research and applications related to asynchronous commercial games. Here we introduce Lamarckian - an open-source platform featuring support for evolutionary reinforcement learning scalable to distributed computing resources. To improve the training speed and data efficiency, Lamarckian adopts optimized communication methods and an asynchronous evolutionary reinforcement learning workflow. To meet the demand for an asynchronous interface by commercial games and various methods, Lamarckian tailors an asynchronous Markov Decision Process interface and designs an object-oriented software architecture with decoupled modules. In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: i) both the sampling efficiency and training speed are doubled when running PPO on Google football game;ii) the training speed is 13 times faster when running PBT+PPO on Pong game. Moreover, we also present two use cases: i) how Lamarckian is applied to generating behavior-diverse game AI;ii) how Lamarckian is applied to game balancing tests for an asynchronous commercial game. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

Enhancing Motor Imagery EEG Signal Classification with Simpl...

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International Winter Workshop on brain-computer interface (BCI)

作者： Lu Wang Junkongshuai Wang Bo Wen Wei Mu Lusheng Liu Jiaguan Han Lihua Zhang Jie Jia Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical NeurobiologyAcademy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-MedicalFudan University Shanghai China Ji Hua Laboratory Guangdong Province Foshan China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

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Tagging Continuous Labels for EEG-based Emotion Classification

Tagging Continuous Labels for EEG-based Emotion Classificati...

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

作者： Rong-Fei Gu Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People’s Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People’s Republic of China

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

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Nucleus accumbens-linked executive control networks mediating reversal learning in tree shrew brain

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Zoological Research 2022年第4期43卷 528-531页

作者： Ting-Ting Pan Chao Liu De-Min Li Bin-Bin Nie Tian-Hao Zhang Wei Zhang Shi-Lun Zhao Qi-Xin Zhou Hua Liu Gao-Hong Zhu Lin Xu Bao-Ci Shan School of Physics and Microelectronics Zhengzhou UniversityZhengzhouHenan 450001China Beijing Engineering Research Center of Radiographic Techniques and Equipment Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Animal Models and Human Disease Mechanisms and KIZ-SU Joint Laboratory of Animal Model and Drug Developmentand Laboratory of Learning and MemoryKunming Institute of ZoologyChinese Academy of SciencesKunmingYunnan 650223China Department of Nuclear Medicine First Affiliated Hospital of Kunming Medical UniversityKunmingYunnan 650032China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China Kunming College of Life Science University of Chinese Academy of SciencesKunmingYunnan 650204China CAS Centre for Excellence in Brain Science and Intelligent Technology Shanghai 200031China

DEAR EDITOR,Cognitive flexibility is crucial for animal survival but is frequently impaired in neuropsychiatric *** many brain structures and functional networks are involved in cognitive flexibility,the neural mechanisms underlying cooperation among specific functional networks remain unclear from a global *** this study,[^(18)F]-fluorodeoxyglucose positron emission tomography(FDG-PET)was performed on 19 male tree shrews after four different visual discrimination tasks,including baseline,learning expert(LE),reversal naive(RN),and reversal expert(RE).

关键词： networks EDIT tree

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Light Radiation Simulation of Micro-LED Array for the Optical Neural interface Targeted Cerebral Cortex in Rhesus Monkey

Light Radiation Simulation of Micro-LED Array for the Optica...

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International Winter Workshop on brain-computer interface (BCI)

作者： Lusheng Liu Wenlei Zhang Bo Wen Minjie Wang Aiping Wang Jing Zhang Yuan Zhang Song Le Haifeng Jiang Rongwei Zhai Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China Shanghai Mental Health Center Shanghai China Jiao Tong University School of Medicine Shanghai China Lingang Laboratory Shanghai China

The advanced technology of optogenetics has greatly promoted the development of neuroscience. To further optimize the optical neural interface, understanding the propagation process of light in neural tissues is essential for neuroscience research on the application of LED in optogenetics. In this work, we use TracePro software to build geometric models and simulate LED devices. The Rhesus monkey brain model is established to simulate the neural tissue to analyze the irradiance distribution at a specific location, to obtain the effective lighting range. In addition, we analyzed the influencing factors of the illumination range of the implanted light source device. We believe that the distance between devices should be $1.1 \sim1.2$ times of the light range of the simulation result when the devices fit the brain.

关键词： Neuroscience Shape Simulation Lighting Implants Light emitting diodes Optogenetics

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