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排序方式: 共有811条查询结果,搜索用时 15 毫秒
1.
当代风景园林研究密切关注共性心理与景观环境的交互关系,景观关注度成为景观心理描述与环境绩效的重要评价指标。基于风景园林"环境-心理"耦合机制与脑电信号采集技术(EEG)研究进展,结合景观关注度研究相关成果,基于主成分分析法建立了一种景观关注度脑电数据回归模型。以玄武湖公园为例,通过主成分分析法对原始景观因子进行整合筛选,建立变量观测体系并对其景观心理评价要素进行脑电分析,通过算法设定与数据分析获取景观关注度主成分及其量化耦合关系。研究结果表明,玄武湖公园景观色彩丰富度、空间集成度、水体形态变化度3项评价指标对景观关注度起主要作用,景观关注度与景观因子种类呈正相关关系。研究结果为景观心理量化描述与算法分析提供了一种基于脑电数据的景观关注度分析技术,为相关研究纵深开展提供技术支持。  相似文献   
2.
Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.  相似文献   
3.
胡章芳  张力  黄丽嘉  罗元 《计算机应用》2019,39(8):2480-2483
针对目前运动想象脑电(EEG)信号识别率较低的问题,考虑到脑电信号蕴含着丰富的时频信息,提出一种基于时频域的卷积神经网络(CNN)运动想象脑电信号识别方法。首先,利用短时傅里叶变换(STFT)对脑电信号的相关频带进行预处理,并将多个电极的时频图组合构造出一种二维时频图;然后,针对二维时频图的时频特性,通过一维卷积的方法设计了一种新颖的CNN结构;最后,通过支持向量机(SVM)对CNN提取的特征进行分类。基于BCI数据集的实验结果表明,所提方法的平均识别率为86.5%,优于其他传统运动想象脑电信号识别方法;同时将该方法应用在智能轮椅上,验证了其有效性。  相似文献   
4.
摘要:致痫区脑电识别能够为癫痫外科手术提供重要的参考价值。提出了一种基于深度网络迁移学习的致痫区脑电识别算法。首先利用连续小波变换(CWT)对脑电信号进行时频分析,获得脑电信号时频图;然后迁移学习AlexNet网络模型,调整网络结构使之适应于致痫区脑电识别,将模型第7层全连接层输出作为脑电信号时频图的特征表示,最后利用支持向量机(SVM)、BP神经网络、长短期记忆网络(LSTM)、基于稀疏表达分类算法(SRC)、线性判别分析(LDA)等分类算法进行特征分类。基于开源脑电数据集采用十折交叉验证的方法对算法进行了验证,比较6种分类器的效果,得到SVM算法的平均特异性为8881%,灵敏度为8807%,准确率为8844%,证明了该方法识别致痫区脑电信号的有效性。 .txt  相似文献   
5.
基于改进GHSOM的运动想象脑电信号自适应识别方法   总被引:1,自引:0,他引:1       下载免费PDF全文
为解决运动想象脑电信号(MI-EEG)的识别方法泛化能力受限和自适应性差等问题,对传统的生长、分层自组织映射神经网络(GHSOM)进行改进,并提出一种主成分分析法(PCA)与改进的GHSOM神经网络(IGHSOM)相结合的脑电自适应识别方法。由于IGHSOM能够根据上一层扩展神经元的量化误差进行自动分层判断,使得其不仅对数据映射更加准确和详细,而且增强了网络的稳定性和自适应性。基于脑机接口(BCI)竞赛数据库,利用PCA进行特征提取,以IGHSOM为分类器进行实验研究。结果表明,该方法获得了较高的识别精度,验证了GHSOM改进策略及该识别方法的正确性和有效性。  相似文献   
6.
针灸是基于传统中医的理论,经临床实践已证明其疗效,然而其作用机制仍不清楚。磁刺激穴位为研究针灸理论提供了一种新的方法。基于图论的复杂网络的构建和分析方法可以帮助研究脑功能网络的拓扑结构和理解大脑的工作机制。在该研究中,通过磁刺激内关穴(PC6)采集EEG信号;运用非线性动力学方法(近似熵)和复杂网络理论,基于磁刺激内关穴的脑电信号构建脑功能网络并对脑功能网络进行分析;对比分析了安静和磁刺激两种状态下的脑功能网络的拓扑性质。实验结果表明,基于刺激内关穴构建的脑功能网络,其拓扑结构发生了改变,网络连接增强,信息传输效率提高,并且"小世界"属性增强。  相似文献   
7.
While creativity is essential for developing students’ broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students’ creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity.  相似文献   
8.
头皮脑电信号具有非平稳特性,相干等传统分析方法并不能很好地检测这些脑电时间序列间的依赖关系。广义同步中的似然同步算法对非平稳信号处理具有较好的效果,该文将它应用到实际脑电信号分析中。基于单向耦合Henon映射系统和实际脑电数据的仿真结果均表明,基于广义同步的似然同步方法适用测量非平稳信号间关系。针对健康被试静息态下,从闭眼到睁眼的过程中脑电信号间同步性的变化进行了研究,发现从闭眼到睁眼过程中,大脑的alpha波在几乎所有电极间的同步强度都显著地减弱,大脑的活动受到一定的抑制。上述结果也表明该方法在脑电数据分析中具有重要的意义,为其他的脑电研究提供一定的参考方法。  相似文献   
9.
Signatures have long been considered to be one of the most accepted and practical means of user verification, despite being vulnerable to skilled forgers. In contrast, EEG signals have more recently been shown to be more difficult to replicate, and to provide better biometric information in response to known a stimulus. In this paper, we propose combining these two biometric traits using a multimodal Siamese Neural Network (mSNN) for improved user verification. The proposed mSNN network learns discriminative temporal and spatial features from the EEG signals using an EEG encoder and from the offline signatures using an image encoder. Features of the two encoders are fused into a common feature space for further processing. A Siamese network then employs a distance metric based on the similarity and dissimilarity of the input features to produce the verification results. The proposed model is evaluated on a dataset of 70 users, comprised of 1400 unique samples. The novel mSNN model achieves a 98.57% classification accuracy with a 99.29% True Positive Rate (TPR) and False Acceptance Rate (FAR) of 2.14%, outperforming the current state-of-the-art by 12.86% (in absolute terms). This proposed network architecture may also be applicable to the fusion of other neurological data sources to build robust biometric verification or diagnostic systems with limited data size.  相似文献   
10.
The driver''s intention is recognized by electroencephalogram(EEG) signals under different driving conditions to provide theoretical and practical support for the applications of automated driving. An EEG signal acquisition system is established by designing a driving simulation experiment, in which data of the driver''s EEG signals before turning left, turning right, and going straight, are collected in a specified time window. The collected EEG signals are analyzed and processed by wavelet packet transform to extract characteristic parameters. A driving intention recognition model, based on neural network, is established, and particle swarm optimization (PSO) is adopted to optimize the model parameters. The extracted characteristic parameters are inputted into the recognition model to identify driving intention before turning left, turning right, and going straight. Matlab is used to simulate and verify the established model to obtain the results of the model.The maximum recognition rate of driving intention is 92.9%. Results show that the driver''s EEG signal can be used to analyze the law of EEG signals. Furthermore, the PSO-based neural network model can be adapted to recognize driving intention.  相似文献   
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