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Inter-Participant Transfer Learning with Attention Based Domain Adversarial Training for P300 Detection

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SSRN

SSRN 2024年

作者： Li, Shurui Daly, Ian Guan, Cuntai Cichocki, Andrzej Jin, Jing Center of Intelligent Computing School of Mathematics East China University of Science and Technology Shanghai200237 China Brain-Computer Interfacing Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Nanyang Technological University 639798 Singapore RIKEN Brain Science Institute Wako351–0198 Japan Torun87-100 Poland Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

A brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which require training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source Domain transfer based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for participant-independent ERP classification tasks. We train and validate our proposed model using both a publicly available OpenBMI dataset and a self-collected dataset, employing a leave-one-participant-out cross-validation scheme. Our experimental results demonstrate that our OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowCovNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method. © 2024, The Authors. All rights reserved.

关键词： brain computer interface

ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling

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arXiv 2023年

作者： Zhan, Qiugang Tao, Ran Xie, Xiurui Liu, Guisong Zhang, Malu Tang, Huajin Yang, Yang The Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China The Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The College of Computer Science The State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou310027 China

In recent years, studies on image generation models of spiking neural networks (SNNs) have gained the attention of many researchers. Variational autoencoders (VAEs), as one of the most popular image generation models, have attracted a lot of work exploring their SNN implementation. Due to the constrained binary representation in SNNs, existing SNN VAE methods implicitly construct the latent space by an elaborated autoregressive network and use the network outputs as the sampling variables. However, this unspecified implicit representation of the latent space will increase the difficulty of generating high-quality images and introduce additional network parameters. In this paper, we propose an efficient spiking variational autoencoder (ESVAE) that constructs an interpretable latent space distribution and designs a reparameterizable spiking sampling method. Specifically, we construct the prior and posterior of the latent space as a Poisson distribution using the firing rate of the spiking neurons. Subsequently, we propose a reparameterizable Poisson spiking sampling method, which is free from the additional network. Comprehensive experiments have been conducted, and the experimental results show that the proposed ESVAE outperforms previous SNN VAE methods in reconstructed & generated image quality. In addition, experiments demonstrate that the encoder of ESVAE can retain the original image information more efficiently and is more robust. The source code is available at https://***/QgZhan/ESVAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Poisson distribution

Simulation and assimilation of the digital human brain

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arXiv 2022年

作者： Lu, Wenlian Du, Xin Wang, Jiexiang Zeng, Longbin Ye, Leijun Xiang, Shitong Zheng, Qibao Zhang, Jie Xu, Ningsheng Feng, Jianfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence [Fudan University Ministry of Education China Shanghai Centre for Mathematical Sciences Fudan University Shanghai China School of Computer Science Fudan University Shanghai China School of Software Technology Zhejiang University Hangzhou China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom Zhangjiang Fudan International Innovation Centre Shanghai China

Here, we present the Digital brain (DB), a platform for simulating spiking neuronal networks at the large neuron scale of the human brain based on personalized magnetic-resonance-imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs, including a two-level routing scheme between GPUs to accelerate spike transmission up to 47.8 trillion neuronal synapses, was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting-state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Neurons

DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

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arXiv 2023年

作者： Li, Zongrui Zheng, Qian Shi, Boxin Pan, Gang Jiang, Xudong School of Electrical and Electronic Engineering Nanyang Technological University Singapore The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Reflection

DMNER: Biomedical Named Entity Recognition by Detection and Matching

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arXiv 2023年

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Zhou, Hong Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Atypon Systems LLC United Kingdom Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAP-BERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER. Copyright © 2023, The Authors. All rights reserved.

关键词： Character recognition

Spiking Neural Network Analysis of Mt-Mst Pathways in Biological Motion Processing

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SSRN

SSRN 2024年

作者： Zhang, Yun Liu, Ying Feng, Tingting Zhang, Tao Qu, Hong Yi, Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Psychology University of Chinese Academy of Sciences Beijing100049 China College of Computer Science Sichuan University Chengdu610065 China

Understanding the neural mechanisms underlying biological motion perception remains a significant challenge in neuroscience. To further explore this mechanism, we construct the BioMotion-SNN model using real bioneural data from the MT to MST regions in macaques. To characterize neuron activity within specific time windows, we propose the windows learning strategy, which employs windowed learning to extract crucial information related to specific events or stimuli. By analyzing the connectivity structure of the BioMotion-SNN model, we identify regular projection patterns from MT to MST, reflected in the varying response characteristics of MT neurons based on their projection strength to different MST neuron populations. This work not only verifies some biological properties of MST neurons, but also underscores the potential of neurodevelopmental SNN models, driven byreal electrophysiological data, in improving our understanding of neural processing. © 2024, The Authors. All rights reserved.

关键词： Neural networks

A survey on neural network interpretability

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arXiv 2020年

作者： Zhang, Yu Tiňo, Peter Leonardis, Aleš Tang, Ke The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trust-Worthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g., algorithmic discrimination. Moreover, interpretability is a desired property for deep networks to become powerful tools in other research fields, e.g., drug discovery and genomics. In this survey, we conduct a comprehensive review of the neural network interpretability research. We first clarify the definition of interpretability as it has been used in many different contexts. Then we elaborate on the importance of interpretability and propose a novel taxonomy organized along three dimensions: type of engagement (passive vs. active interpretation approaches), the type of explanation, and the focus (from local to global interpretability). This taxonomy provides a meaningful 3D view of distribution of papers from the relevant literature as two of the dimensions are not simply categorical but allow ordinal subcategories. Finally, we summarize the existing interpretability evaluation methods and suggest possible research directions inspired by our new taxonomy. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

Assessment of causal effects of physical activity on neurodegenerative diseases:A Mendelian randomization study

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Journal of Sport and Health science 2021年第4期10卷 454-461页

作者： Peng-Fei Wu Hui Lu Xiaoting Zhou Xuchen Liang Ruizhuo Li Wan Zhang Danyang Li Kun Xia Center for Medical Genetics&Hunan Provincial Key Laboratory of Medical Genetics School of Life SciencesCentral South UniversityChangsha 410008China Department of Neurology Beth Israel Deaconess Medical Center&Harvard Medical SchoolBostonMA 02115USA Department of Neurology Xuanwu HospitalCapital Medical UniversityBeijing 100053China Department of Neurology Xiangya HospitalCentral South UniversityChangsha 410008China School of Physical Education Henan UniversityKaifeng 475001China School of Medicine South China University of TechnologyGuangzhou 510006China Department of Biology College of Arts&SciencesBoston UniversityBostonMA 02215USA CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China

Background:Physical activity has been hypothesized to play a protective role in neurodegenerative ***,effect estimates previously derived from observational studies were prone to confounding or reverse ***:We performed a two-sample Mendelian randomization(MR)analysis to explore the causal association of accelerometer-measured physical activity with 3 common neurodegenerative diseases:Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis(ALS).We selected genetic instrumental variants reaching genome-wide significance(p<5×10^(-8))from 2 largest meta-analyses of about 91,100 UK Biobank *** statistics for AD,PD,and ALS were retrieved from the up-to-date studies in European ancestry led by the international *** random-effect,inverse-variance weighted MR was employed as the primary method,while MR pleiotropy residual sum and outlier(MR-PRESSO),weighted median,and MR-Egger were implemented as sensitivity *** statistical analyses were performed using the R programming language(Version 3.6.1;R Foundation for Statistical Computing,Vienna,Austria).Results:Primary MR analysis and replication analysis utilized 5 and 8 instrumental variables,which explained 0.2%and 0.4%variance in physical activity,*** each set,one variant at 17q21 was significantly associated with PD,and MR sensitivity analyses indicated them it as an outlier and source of heterogeneity and *** results with the removal of outlier variants suggested odds ratios(ORs)of neurodegenerative diseases per unit increase in objectively measured physical activity were 1.52 for AD(95%confidence interval(95%CI):0.88-2.63,p=0.13)and 3.35 for PD(95%CI:1.32-8.48,p=0.01),while inconsistent results were shown in the replication set for AD(OR=1.06,95%CI:1.01-1.12,p=0.02)and PD(OR=0.99,95%CI:0.88-0.12,p=0.97).Similarly,the beneficial effect of physical activity on ALS(OR=0.51,95%CI:0.29-0.91,p=0.02)was not confirmed in

关键词： Alzheimer’s disease Amyotrophic lateral sclerosis Genetic epidemiology Mendelian randomization Parkinson’s disease Physical activity

Let's play music: Audio-driven performance video generation

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arXiv 2020年

作者： Zhu, Hao Li, Yi Zhu, Feixia Zheng, Aihua He, Ran Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China CASIA Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multistaged framework to achieve this new task to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap offers more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and inter-frame temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Music