检索结果-内蒙古大学图书馆

10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024

作者： Dixen, Laurits Heinrich, Stefan Burelli, Paolo IT University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031824869

neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. The THINGS initiative [11] provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning models. We create a derivative dataset from this of living vs non-living entities test 15 different deep learning models with 5 different architectures and compare to a SOTA linear model. We show that the linear model is not able to solve the decoding task, while almost all the deep learning models are successful, suggesting that in some cases non-linear models are needed to decode neural representations. We also run a comparative study of the models’ performance on individual object categories, and suggest how artificial neural networks can be used to study brain activity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmark Deep Learning EEG neural decoding THINGS

来源：评论

学校读者我要写书评

暂无评论

Real-Time Myoelectric-Based neural-Drive decoding for Concurrent and Continuous Control of Robotic Finger Forces

引用

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2025年第2期55卷 256-265页

作者： Meng, Long Vargas, Luis Kamper, Derek G. Hu, Xiaogang Penn State Univ Dept Mech Engn University Pk PA 16802 USA Univ North Carolina Joint Dept Biomed Engn Chapel Hill NC 27599 USA North Carolina State Univ Raleigh NC 27695 USA Penn State Univ Dept Kinesiol University Pk PA 16802 USA Penn State Univ Dept Phys Med & Rehabil University Pk PA 16802 USA Penn State Univ Huck Inst Life Sci Ctr Neural Engn University Pk PA 16802 USA

neural or muscular injuries, such as due to amputation, spinal cord injury, and stroke, can affect hand functions, profoundly impacting independent living. This has motivated the advancement of cutting-edge assistive robotic hands. However, unintuitive myoelectric control of these devices remains challenging, which limits the clinical translation of these devices. Accordingly, we developed a robust motor-intent decoding approach to continuously predict the intended fingertip forces of single and multiple fingers in real time. We used population motor neuron discharge activities (i.e., neural drive from brain to spinal cord) decoded from a high-density surface electromyogram (HD-sEMG) signals as the control signals instead of the conventional global sEMG features. To enable real-time neural-drive prediction, we employed a convolutional neural network model to establish the mapping from global HD-sEMG features to finger-specific neural-drive signals, which were then employed for continuous and real-time control of three prosthetic fingers (index, middle, and ring). As a result, the neural-drive-based approach can decode the motor intent of single-finger and multifinger forces with significantly lower force estimation errors than that obtained using the global HD-sEMG-amplitude approach. Besides, the force prediction accuracy was consistent over time and demonstrated strong robustness to signal interference. Our network-based decoder can also achieve better finger isolation with minimal forces predicted in unintended fingers. Our work demonstrates that the accurate and robust finger force control could be achieved through this new decoding approach. The outcomes offer an efficient intent prediction approach that allows users to have intuitive control of prosthetic fingertip forces in a dexterous way.

关键词： Discharges (electric) Force Hands Feature extraction decoding Motors Real-time systems Muscles Neurons Indexes Force control hand dexterity hand function neural decoding robotic hand

来源：评论

学校读者我要写书评

暂无评论

neural decoding of electrocorticographic signals using dynamic mode decomposition

引用

JOURNAL OF neural ENGINEERING 2020年第3期17卷 036009-036009页

作者： Shiraishi, Yoshiyuki Kawahara, Yoshinobu Yamashita, Okito Fukuma, Ryohei Yamamoto, Shota Saitoh, Youichi Kishima, Haruhiko Yanagisawa, Takufumi Osaka Univ Inst Adv Cocreat Studies 2-2 Yamadaoka Suita Osaka 5650871 Japan RIKEN Ctr Adv Intelligence Project Chuo Ku Nihonbashi 1 Chome Mitsui Bldg15th Floor Tokyo 1030027 Japan Kyushu Univ Inst Math Ind Nishi Ku 744 Motooka Fukuoka 8190395 Japan ATR Neural Informat Anal Labs Dept Computat Brain Imaging 2-2-2 Hikaridai Seika Kyoto 6190288 Japan Osaka Univ Dept Neurosurg Grad Sch Med 2-2 Yamadaoka Suita Osaka 5650871 Japan Osaka Univ Dept Neuromodulat & Neurosurg Grad Sch Med 2-2 Yamadaoka Suita Osaka 5650871 Japan ATR Computat Neurosci Labs Dept Neuroinformat 2-2-2 Hikaridai Seika Kyoto 6190288 Japan

Objective. Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals have been developed to restore the communication function of severely paralyzed patients. However, the limited amount of information derived from ECoG signals hinders their clinical applications. We aimed to develop a method to decode ECoG signals using spatiotemporal patterns characterizing movement types to increase the amount of information gained from these signals. Approach. Previous studies have demonstrated that motor information could be decoded using powers of specific frequency bands of the ECoG signals estimated by fast Fourier transform (FFT) or wavelet analysis. However, because FFT is evaluated for each channel, the temporal and spatial patterns among channels are difficult to evaluate. Here, we used dynamic mode decomposition (DMD) to evaluate the spatiotemporal pattern of ECoG signals and evaluated the accuracy of motor decoding with the DMD modes. We used ECoG signals during three types of hand movements, which were recorded from 11 patients implanted with subdural electrodes. From the signals at the time of the movements, the modes and powers were evaluated by DMD and FFT and were decoded using support vector machine. We used the Grassmann kernel to evaluate the distance between modes estimated by DMD (DMD mode). In addition, we decoded the DMD modes, in which the phase components were shuffled, to compare the classification accuracy. Main results. The decoding accuracy using DMD modes was significantly better than that using FFT powers. The accuracy significantly decreased when the phases of the DMD mode were shuffled. Among the frequency bands, the DMD mode at approximately 100 Hz demonstrated the highest classification accuracy. Significance. DMD successfully captured the spatiotemporal patterns characterizing the movement types and contributed to improving the decoding accuracy. This method can be applied to improve BCIs to help severely paralyzed patients co

关键词： dynamic mode decomposition electrocorticography neural decoding Grassmann kernel principal angle

来源：评论

学校读者我要写书评

暂无评论

neural decoding for Macaque's Finger Position: Convolutional Space Model

引用

IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2019年第3期27卷 543-551页

作者： Wu, Haifeng Feng, Jingyi Zeng, Yu Yunnan Minzu Univ Dept Elect & Informat Engn Kunming 650500 Yunnan Peoples R China

In this paper, we study how to use the number of spike signals in a macaque's motor cortex to estimate the position of its finger movement. First, we analyze the time correlation of a traditional state space model (SSM) and derive a convolutional space model (CSM) to decode the movement position of the macaque finger. Compared with the traditional model, the model can correlate the current moment state with the previous moment. In addition, we have improved the original SSM model using the CSM model. In its observation equation, the number of spike signals at one is expanded to the number of spike signals at multiple previous moments. In this way, the current time state in the improved model can be related to the number of spike signals at a plurality of previous times. In the experiment, a group of public data are used to validate the decoding performance of the model, and a least squares, a batch recursive least squares, and a gradient descent algorithms are used to train the model. The experimental results show that the decoding error of the CSM model and the improved SSM model is smaller than that of the traditional model, and thus, they have a better decoding accuracy. The results show that the CSM model has improved about 11.7% in x-axis decoding error performance than the traditional models.

关键词： neural decoding TILM model SSM model CSM model

来源：评论

学校读者我要写书评

暂无评论

neural decoding of Hand Motion Using a Linear State-Space Model With Hidden States

引用

IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2009年第4期17卷 370-378页

作者： Wu, Wei Kulkarni, Jayant E. Hatsopoulos, Nicholas G. Paninski, Liam Florida State Univ Dept Stat Tallahassee FL 32306 USA Columbia Univ Ctr Theoret Neurosci New York NY 10027 USA Univ Chicago Dept Organismal Biol & Anat Chicago IL 60637 USA Columbia Univ Dept Stat New York NY 10027 USA

The Kalman filter has been proposed as a model to decode neural activity measured from the motor cortex in order to obtain real-time estimates of hand motion in behavioral neurophysiological experiments. However, currently used linear state-space models underlying the Kalman filter do not take into account other behavioral states such as muscular activity or the subject's level of attention, which are often unobservable during experiments but may play important roles in characterizing neural controlled hand movement. To address this issue, we depict these unknown states as one multidimensional hidden state in the linear state-space framework. This new model assumes that the observed neural firing rate is directly related to this hidden state. The dynamics of the hand state are also allowed to impact the dynamics of the hidden state, and vice versa. The parameters in the model can be identified by a conventional expectation-maximization algorithm. Since this model still uses the linear Gaussian framework, hand-state decoding can be performed by the efficient Kalman filter algorithm. Experimental results show that this new model provides a more appropriate representation of the neural data and generates more accurate decoding. Furthermore, we have used recently developed computationally efficient methods by incorporating a priori information of the targets of the reaching movement. Our results show that the hidden-state model with target-conditioning further improves decoding accuracy.

关键词： Hidden states Kalman filter motor cortex neural decoding state-space model

来源：评论

学校读者我要写书评

暂无评论

neural decoding of Visual Information Across Different neural Recording Modalities and Approaches

引用

Machine Intelligence Research 2022年第5期19卷 350-365页

作者： Yi-Jun Zhang Zhao-Fei Yu Jian.K.Liu Tie-Jun Huang Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science Peking UniversityBeijing 100190China Institute for Artificial Intelligence Peking UniversityBeijing 100190China School of Computing University of LeedsLeeds LS29JTUK Beijing Academy of Artificial Intelligence Beijing 100190Chin

Vision plays a peculiar role in *** information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent *** advances have led to the development of brain-inspired algorithms and models for machine *** of the key components of these methods is the utilization of the computational principles underlying biological ***,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual ***,there is a high demand for mapping out functional models for reading out visual information from neural ***,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.

关键词： neural decoding machine learning deep learning visual decoding brain-inspired vision

来源：评论

学校读者我要写书评

暂无评论

neural decoding based on probabilistic neural network

引用

Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2010年第4期11卷 298-306页

作者： Yi YU Shao-min ZHANG Huai-jian ZHANG Xiao-chun LIU Qiao-sheng ZHANG Xiao-xiang ZHENG Jian-hua DAI Qiushi Academy for Advanced Studies Zhejiang University Hangzhou 310027 China College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 31002 7 China Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University Hangzhou 310027 China College of Computer Science and Technology Zhejiang University Hangzhou 310027 China

Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.

关键词： Brain-machine interfaces (BMI) neural decoding Probabilistic neural network (PNN) Microelectrode array

来源：评论

学校读者我要写书评

暂无评论

neural decoding of Finger Movements Using Skellam-Based Maximum-Likelihood decoding

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2010年第3期57卷 754-760页

作者： Shin, Hyun-Chool Aggarwal, Vikram Acharya, Soumyadipta Schieber, Marc H. Thakor, Nitish V. Johns Hopkins Sch Med Dept Biomed Engn Baltimore MD 21205 USA Soongsil Univ Dept Elect Engn Coll Informat Technol Seoul 1560743 South Korea Univ Rochester Med Ctr Dept Neurol Rochester NY 14642 USA Univ Rochester Med Ctr Dept Neurobiol & Anat Rochester NY 14642 USA Univ Rochester Med Ctr Dept Brain & Cognit Sci Rochester NY 14642 USA Univ Rochester Med Ctr Dept Phys Med & Rehabil Rochester NY 14642 USA

We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a nonhuman primate during single finger movements. The method is based on the maximum-likelihood (ML) inference, which assuming the probability of finger movements is uniform, is equivalent to the maximum a posteriori ( MAP) inference. Each neuron's activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement, i.e., Pr(neuronal activation (x)vertical bar finger movements ( m)). Based on the ML criterion, we choose finger movements to maximize Pr(x vertical bar m). Experimentally, data were collected from 115 task-related neurons in M1 as the monkey performed flexion and extension of each finger and the wrist ( 12 movements). With as few as 20-25 randomly selected neurons, the proposed method decoded single-finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand.

关键词： Finger movements maximum likelihood neural decoding neural prosthetics Skellam

来源：评论

学校读者我要写书评

暂无评论

neural decoding on imbalanced calcium imaging data with a network of support vector machines

引用

ADVANCED ROBOTICS 2021年第7期35卷 459-470页

作者： Lee, Kyunghun Wu, Xiaomin Lee, Yaesop Lin, Da-Ting Bhattacharyya, Shuvra S. Chen, Rong Univ Maryland Sch Med Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA NIDA Intramural Res Program NIH Baltimore MD USA

We present a novel neural decoding system for calcium imaging data. Miniature calcium imaging is of great utility for examining population neural activity of animals. Our neural decoding system is developed using a carefully designed support vector machine subsystem together with dataflowbased techniques for system design, which capture the high-level structure of the application and enable powerful system-level analysis and optimization. Also, weintroduce a framework for handling imbalanced data. This addresses a problem of imbalanced datasets, which arises commonly in neural decoding applications, as well as in a wide variety of other applications in biomedical engineering and advanced robotics. We developed an ensemble learning-based method to tackle this problem. The proposed framework systemically incorporates two heterogeneous model characteristics into a combined model. Through extensive experiments, we evaluate the proposed system using calcium imaging datasets in which neural activities of D-1 medium spiny neurons in the dorsal striatum were recorded. The results show that the F-1 score of the proposed system is significantly better than those of previously developed neural decoding systems for calcium imaging.

关键词： neural decoding Dataflow Imbalanced data Support vector machine

来源：评论

学校读者我要写书评

暂无评论

neural decoding of collective wisdom with multi-brain computing

引用

NEUROIMAGE 2012年第1期59卷 94-108页

作者： Eckstein, Miguel P. Das, Koel Pham, Binh T. Peterson, Matthew F. Abbey, Craig K. Sy, Jocelyn L. Giesbrecht, Barry Univ Calif Santa Barbara Dept Psychol & Brain Sci Santa Barbara CA 93101 USA Univ Calif Santa Barbara Inst Collaborat Biotechnol Santa Barbara CA 93101 USA

Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally, o

关键词： Multi-brain computing Collective wisdom neural decoding Perceptual decisions Group decisions

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：