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TechRxiv 2022年

作者： Zhang, Rui Yao, Dezhong Cao, Lijun Xu, Zongxin Zhang, Yangsong Zhang, Lipeng Hu, Yuxia Chen, Mingming Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu611731 China

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. In addition, we designed a new SSVEP recognition algorithm, named ensemble online adaptive CCA (eOACCA), to solve the accuracy loss problem in high ambient brightness. The main strategy of eOACCA is to provide initial filters for high-intensity data by iteratively learning low-light-intensity SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities (>600 lux). Compared with FBCCA, the accuracies of eOACCA under 1200 lux was increased by 12.83%. In conclusion, current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful to promote the AR-BCI application in complex lighting environments. © 2022-11-30, CC BY.

关键词： Augmented reality

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Multi-View Self-Supervised Learning Enhances Automatic Sleep Staging from EEG Signals

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Yu, Tianyou Hu, Xinxin He, Yanbin Wu, Wei Gu, Zhenghui Yu, Zhuliang Li, Yuanqing Wang, Fei Xiao, Jun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Pazhou Laboratory Research Center for Brain-Computer Interface Guangzhou510330 China Guangdong Work Injury Rehabilitation Hospital Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangzhou510440 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China South China Normal University School of Software Guangzhou510631 China South China University of Technology School of Electric Power Engineering Guangzhou510640 China

Deep learning-based methods for automatic sleep staging offer an efficient and objective alternative to costly manual scoring. However, their reliance on extensive labeled datasets and the challenge of generalization to new subjects and datasets limit their widespread adoption. Self-supervised learning (SSL) has emerged as a promising solution to address these issues by learning transferable representations from unlabeled data. This study highlights the effectiveness of SSL in automated sleep staging, utilizing a customized SSL approach to train a multi-view sleep staging model. This model includes a temporal view feature encoder for raw EEG signals and a spectral view feature encoder for time-frequency features. During pretraining, we incorporate a cross-view contrastive loss in addition to a contrastive loss for each view to learn complementary features and ensure consistency between views, enhancing the transferability and robustness of learned features. A dynamic weighting algorithm balances the learning speed of different loss components. Subsequently, these feature encoders, combined with a sequence encoder and a linear classifier, enable sleep staging after finetuning with labeled data. Evaluation on three publicly available datasets demonstrates that finetuning the entire SSL-pretrained model achieves competitive accuracy with state-of-the-art methods—86.4%, 83.8%, and 85.5% on SleepEDF-20, SleepEDF-78, and MASS datasets, respectively. Notably, our framework achieves near-equivalent performance with only 5% of the labeled data compared to full-label supervised training, showcasing SSL's potential to enhance automated sleep staging efficiency. © 1964-2012 IEEE.

关键词： Self-supervised learning

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Research on Indoor Localization Algorithm for Emergency Rescue Personnel Based on INS/UWB

Research on Indoor Localization Algorithm for Emergency Resc...

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Electrical, Electronic Information and Communication Engineering (EEICE), International Conference on

作者： Mingyue Zhang Cheng Peng Guan Li Xiangli Bu Yang Wang North China Institute of Science and Technology School of Information and Control Engineering Key Laboratory of Brain-Computer Interface Technology Application of the Ministry of Emergency Management Langfang City China School of Information and Control Engineering North China Institute of Science and Technology Langfang City China Big Data Center Ministry of Emergency Management Beijing China School of Emergency Equipment North China Institute of Science and Technology Langfang City China

ISBN: (数字)9798331532598

ISBN: (纸本)9798331532604

To address the problem of low rescuer positioning accuracy in complex indoor rescue scenarios, a tightly combined positioning algorithm based on an adaptive weighting inertial navigation system/ultra-wideband (INS/UWB) is proposed. The algorithm innovatively integrates deep learning and error-resistant estimation theory and reduces the positioning error through multi-dimensional optimization. A zero velocity detection algorithm is developed based on a hybrid model of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and an attention mechanism, which can accurately detect the zero velocity interval (ZVI) of a person's gait. The Zero Velocity Interval (ZVI) of a person's gait can be accurately recognized with a recognition accuracy of 98.2%, which is 15.7% higher than that of the traditional method. For the indoor UWB non-line-of-sight (NLOS) error, the IGG III error-resistant model is introduced to dynamically adjust the weights of the UWB range values and adaptively adjust the configuration of the system state covariance matrix and the measurement matrix. The experimental results show that under the indoor mixed occlusion conditions, the positioning accuracy is improved by 16.3% compared with the standard EKF algorithm, and the positioning error is controlled within 0.89m, indicating that the adaptive weighting INS/UWB tight combination positioning algorithm proposed in this paper is able to effectively use the IMU and UWB information to achieve a high-precision positioning function in indoor rescue scenarios and meet the positioning requirements in emergency rescue scenarios.

关键词： Location awareness Weight measurement Adaptation models Accuracy Adaptive systems Feature extraction Real-time systems Personnel Covariance matrices Standards

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Biallelic variants in SREBF2 cause autosomal recessive spastic paraplegia

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Journal of Genetics and Genomics 2025年

作者： Wei, Qiao Fan, Wenlu Li, Hong-Fu Wang, Pei-Shan Xu, Man Dong, Hai-Lin Yu, Hao Lyu, Jialan Luo, Wen-Jiao Chen, Dian-Fu Ge, Wanzhong Wu, Zhi-Ying Department of Medical Genetics and Center for Rare Diseases Second Affiliated Hospital Zhejiang University School of Medicine and Zhejiang Key Laboratory of Rare Diseases for Precision Medicine and Clinical Translation Zhejiang Hangzhou 310009 China Nanhu Brain-computer Interface Institute Zhejiang Hangzhou 311100 China Division of Human Reproduction and Developmental Genetics Women's Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310006 China MOE Frontier Science Center for Brain Research and Brain-Machine Integration School of Brain Science and Brain Medicine Zhejiang University Zhejiang Hangzhou 310012 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031 China

Hereditary spastic paraplegias (HSPs) refer to a genetically and clinically heterogeneous group of neurodegenerative disorders characterized by the degeneration of motor neurons. To date, a significant number of patients still have not received a definite genetic diagnosis. Therefore, identifying unreported causative genes continues to be of great importance. Here, we perform whole exome sequencing in a cohort of Chinese HSP patients. Three homozygous variants (p.L604W, p.S517F, and p.T984A) within the sterol regulatory element-binding factor 2 (SREBF2) gene are identified in one autosomal recessive family and two sporadic patients, respectively. Co-segregation is confirmed by Sanger sequencing in all available members. The three variants are rare in the public or in-house database and are predicted to be damaging. The biological impacts of variants in SREBF2 are examined by functional experiments in patient-derived fibroblasts and Drosophila. We find that the variants upregulate cellular cholesterol due to the overactivation of SREBP2, eventually impairing the autophagosomal and lysosomal functions. The overexpression of the mature form of SREBP2 leads to locomotion defects in Drosophila. Our findings identify SREBF2 as a causative gene for HSP and highlight the impairment of cholesterol as a critical pathway for HSP. © 2025 The Authors

关键词： Causative gene Cholesterol Hereditary spastic paraplegia Locomotion defects SREBF2

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Manipulation of Phase Transitions and Electrical Properties of Ba 1-XSr xTiO 3 Thin Films Through Orientation Engineering

SSRN

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SSRN 2023年

作者： Lin, Jiamin Guo, Xiangwei Li, Cheng Hong, Zijian Wu, Yongjun Huang, Yuhui School of Materials Science and Engineering State Key Laboratory of Silicon and Advanced Semiconductor Materials Cyrus Tang Center for Sensor Materials and Applications Zhejiang University Hangzhou310027 China Zhejiang Brain-computer Interface Institute Hangzhou311100 China Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices Hangzhou Innovation Center Zhejiang University Zhejiang Hangzhou311200 China

This study investigates the influence of epitaxial orientation on the phase structures and electrical properties of Barium Strontium Titanate ( Ba1-XSrxTiO3 , BST) thin films by employing a thermodynamic model. A comprehensive analysis is conducted, including the construction of strain-concentration and temperature-strain phase diagrams for (001)-, (110)-, and (111)-oriented BST thin films. The results demonstrate that BST thin films with high-index (110) and (111) orientations have a greater tendency to stabilize the low-symmetry ferroelectric phase compared to the low-index (001) orientation. Subsequently, the impact of epitaxial orientation on the piezoelectric and electrocaloric properties is explored based on the constructed phase diagrams. Significantly, enhanced piezoelectric and electrocaloric effects closely related to the epitaxial orientation are observed at the different phase boundaries for BST thin films. These findings have profound implications for future experimental investigations and the design of devices incorporating BST thin films with precise epitaxial orientations and innovative functionalities. © 2023, The Authors. All rights reserved.

关键词： Phase diagrams

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Data-Driven Output Containment Control of Heterogeneous Multiagent Systems: A Hierarchical Scheme

Data-Driven Output Containment Control of Heterogeneous Mult...

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IEEE Conference on Decision and Control

作者： Malika Sader Wenyu Li Yanhui Yin Zhongmei Li Dexian Huang Zhongxin Liu Xiao He Chao Shang Department of Automation Tsinghua University Beijing China College of Artificial Intelligence Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China School of Automation Engineering North-East Electric Power University Jilin China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this article, we propose a data-driven solution to the output containment control problem of multiagent systems characterized by heterogeneous and unknown dynamics. The proposed data-driven scheme is hierarchical, which includes a network layer and a physical layer, bypassing the traditional modeling exercise and eliminating the need for explicit state-space models. In the network layer, a fully distributed observer without knowing the dynamics of leaders is designed to generate a containment trajectory. A data-driven approach based on data sampled from an auxiliary system is developed to overcome the dependence on the explicit state-space models for design of control gains. This paves the way for deriving a datadriven solution to the regulator equation, which is derived by employing the data informativity condition and relevant data in the physical layer. The results demonstrate that the closedloop system is asymptotically stable, and the regulated output containment converges to zero. Compared to generic modelbased hierarchical schemes, the assumptions that the system matrices are completely known and homogeneous on system dynamics can be relaxed.

关键词： Regulators Protocols System dynamics Distributed feedback devices Observers Physical layer Mathematical models State-space methods Trajectory Multi-agent systems

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Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks

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ISA transactions 2025年

作者： Shicheng Cao Yanhui Yin Wenyu Li Zhongxin Liu Zengqiang Chen College of Artificial Intelligence Nankai University 300350 Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University 300350 Tianjin China. School of Automation Engineering North-East Electric Power University Jilin 132000 China. Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University 300350 Tianjin China. Electronic address: liwenyu@***.

This paper explores the control of time-varying formations in a class of heterogeneous multi-agent systems. The key innovation lies in the simultaneous consideration of hybrid actuator faults and deception attacks. To achieve the control objective, a novel distributed double-layer control scheme, comprising a network layer and a physical layer, is proposed. In the network layer, a distributed observer with secure output feedback control is developed to mitigate severe deception attacks, ensuring that the mean square observer error remains within an acceptable range. In the physical layer, fault compensators are designed to address both additive and multiplicative faults. As a result, the followers achieve time-varying formation control, and closed-loop stability analysis is conducted using the Lyapunov method. Finally, to verify the validity of the theoretical findings, numerical simulations are subsequently conducted.

关键词： Deception attack Fault-tolerant control Heterogeneous system Multi-agent system Time-varying formation control

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Decoding Pigeon Behavior Outcomes during Goal-directed Decision Task by WSR Functional Network Analysis*

Decoding Pigeon Behavior Outcomes during Goal-directed Decis...

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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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BCI Assisted Motor Function Improvement and Recovery in Children with Cerebral Palsy

BCI Assisted Motor Function Improvement and Recovery in Chil...

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Cognitive Sciences, Genomics and Bioinformatics (CSGB)

作者： Pavel Bobrov Sokolova Anastasia Evgeniy Lukyanov Roman Lyukmanov Lajsheva Olga Department. of neuro-computer interfaces Pirogov Russian National Research Medical University Moscow Russia Laboratory of mathematical neurobiology of learning Institute of Higher Nervous Activivty and Neurophysiology RAS Moscow Russia Rehabilitation department Russian Children's Clinical Hospital of Pirogov Russian National Research Medical University Moscow Russia Research Center of Neurology Brain-computer interface group Moscow Russia Department of rehabilitation sports medicine and physical culture Pirogov Russian National Research Medical University Moscow Russia

The paper reports the results of BCI-controlled exoskeleton trials for 88 children and adolescents diagnosed with cerebral palsy and having unilateral paresis or hemiplegia. Significant improvement of motor function according to ARAT Fugl-Meyer, Jebsen-Taylor, and Box and Blocks test were observed, as well as increase in muscle force according to MRCSS. BCI performance indices show that the patients with cerebral palsy were capable of controlling the exoskeleton with accuracy comparable to post-stroke patients. Further analysis indicates that higher gain of the clinical scales is associated with higher F-scores for paretic hand and lower F-scores for intact hand movement imagery classification.

关键词：

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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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