標題: Titlebook: Brain Informatics; 16th International C Feng Liu,Yu Zhang,Hongjun Wang Conference proceedings 2023 The Editor(s) (if applicable) and The Au [打印本頁] 作者: 挑染 時間: 2025-3-21 18:51
書目名稱Brain Informatics影響因子(影響力)
書目名稱Brain Informatics影響因子(影響力)學科排名
書目名稱Brain Informatics網(wǎng)絡(luò)公開度
書目名稱Brain Informatics網(wǎng)絡(luò)公開度學科排名
書目名稱Brain Informatics被引頻次
書目名稱Brain Informatics被引頻次學科排名
書目名稱Brain Informatics年度引用
書目名稱Brain Informatics年度引用學科排名
書目名稱Brain Informatics讀者反饋
書目名稱Brain Informatics讀者反饋學科排名
作者: 入伍儀式 時間: 2025-3-22 00:01
https://doi.org/10.1007/b139104ormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this作者: 蒙太奇 時間: 2025-3-22 02:40 作者: ABOUT 時間: 2025-3-22 07:27 作者: MAG 時間: 2025-3-22 10:19
https://doi.org/10.1007/b139104n focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of co作者: armistice 時間: 2025-3-22 14:01 作者: Indigence 時間: 2025-3-22 18:44
Kontrastmittel in der kardiovaskul?ren MRTesses in terms of spatial and temporal resolution. In this study, we propose a hierarchical deep transcoding model for fusing simultaneous EEG-fMRI data to recover a high spatiotemporal resolution latent neural source space. The model utilizes a cyclic Convolutional Neural Network (CNN) architecture作者: gorgeous 時間: 2025-3-22 22:06
https://doi.org/10.1007/b139104anikin (SAM) when subjects were exposed to videos or images. The classification is performed on electroencephalographic (EEG) signals from the DEAP public dataset, and a dataset collected at the University of Tsukuba, Japan. The experiments were defined to classify low versus high arousal/valence us作者: 暴發(fā)戶 時間: 2025-3-23 03:43 作者: chiropractor 時間: 2025-3-23 05:41
,Tumoren und tumor?hnliche Ver?nderungen,o test modern computational neuroscience hypotheses suggesting that spontaneous brain activity is partially supported by top-down generative processing. A widely studied class of generative models is that of Restricted Boltzmann Machines (RBMs), which can be used as building blocks for unsupervised 作者: 熱情的我 時間: 2025-3-23 11:37 作者: prediabetes 時間: 2025-3-23 17:26
Normale Anatomie und Anlagevarianten,tionship between the performance of ESI methods and the number of scalp EEG electrodes has been a topic of ongoing investigation. Research has shown that high-density EEG is necessary for obtaining accurate and reliable ESI results using conventional ESI solutions, limiting their applications when o作者: 調(diào)整 時間: 2025-3-23 21:02
Normale Anatomie und Anlagevarianten,tivity can be more precise with fMRI data. However, it is still difficult to obtain addiction-related brain connectivity effectively from fMRI data due to the complexity and non-linear characteristics of brain connections. Therefore, this paper proposed a Graph Diffusion Reconstruction Network (GDRN作者: Fortuitous 時間: 2025-3-24 00:09
https://doi.org/10.1007/3-540-32860-2) can detect and track disease progression. However, the majority of MRI data currently available is characterized by low resolution. The present study introduces a novel approach for MRI super-resolution by integrating diffusion model with wavelet decomposition techniques. The methodology proposed 作者: reaching 時間: 2025-3-24 04:55 作者: 思想 時間: 2025-3-24 06:51
,Traumatische Ver?nderungen, Frakturen,as included studies with relatively small sample sizes across research sites, thus limiting inference and the application of novel methods, such as deep learning. To address these issues and facilitate open science, we developed an online platform for data-sharing and advanced research programs to e作者: Arthropathy 時間: 2025-3-24 11:11
,Tumoren und tumor?hnliche L?sionen,pproaches. However, typical ML classifiers are not able to provide information on time and risk to AD conversion. Survival Analysis statistical methods as Cox Proportional Hazard (CPH) give this information and can handle censored data, but they were designed for small dataset and do not perform wel作者: 能得到 時間: 2025-3-24 18:13 作者: 果核 時間: 2025-3-24 20:36
https://doi.org/10.1007/978-3-031-43075-6Cognitive Science; Neuroscience; Machine Learning; Data Science; Artificial Intelligence (AI); Informatio作者: 留戀 時間: 2025-3-24 23:24
978-3-031-43074-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Armada 時間: 2025-3-25 06:26 作者: Synovial-Fluid 時間: 2025-3-25 07:39
Harneshing the?Potential of?EEG in?Neuromarketing with?Deep Learning and?Riemannian Geometrye sample covariance is used as an estimator of the ‘quasi-instantaneous’, brain activation pattern and derived from the multichannel signal recorded while the subject is gazing at a given product. Pattern derivation is followed by proper re-alignment to reduce covariate shift (inter-subject variabil作者: 喚起 時間: 2025-3-25 13:15 作者: 小溪 時間: 2025-3-25 18:11
Measuring Stimulus-Related Redundant and?Synergistic Functional Connectivity with?Single Cell Resolu of redundant and synergystic information carried by these neurons about auditory stimuli. Our findings revealed that functionally connected pairs carry proportionally more redundancy and less synergy than unconnected pairs, suggesting that their functional connectivity is primarily redundant in nat作者: Iatrogenic 時間: 2025-3-25 21:49 作者: 狗舍 時間: 2025-3-26 02:58
Decoding Emotion Dimensions Arousal and?Valence Elicited on?EEG Responses to?Videos and?Images: A Co classification. The obtained difference was confirmed by testing the experiments using a method based on the Discrete Wavelet Transform (DWT) for feature extraction and classification using random forest. Using image-based stimulation may help to better understand low and high arousal/valence when 作者: 小母馬 時間: 2025-3-26 05:33
Investigating the?Generative Dynamics of?Energy-Based Neural Networkses can be increased by initiating top-down sampling from chimera states, which encode high-level visual features of multiple digit classes. We also find that the model is not capable of transitioning between all possible digit states within a single generation trajectory, suggesting that the top-dow作者: 形上升才刺激 時間: 2025-3-26 11:18
Effects of EEG Electrode Numbers on Deep Learning-Based Source Imagingventional ESI methods in computer simulations. Our findings suggest that DeepSIF can provide accurate estimations of the source location and extent across various numbers of channels and noise levels, outperforming conventional methods, which indicates its merits for wide applications of ESI, especi作者: CLAN 時間: 2025-3-26 15:00 作者: 吹牛者 時間: 2025-3-26 17:04
Dyslexia Data Consortium Repository: A Data Sharing and?Delivery Platform for?Researchdevelopment, replicate research findings, apply new methods, and educate the next generation of researchers. The overarching goal of this platform is to advance our understanding of a disorder that has significant academic, social, and economic impacts on children, their families, and society.作者: Exposition 時間: 2025-3-26 23:27
Conversion from Mild Cognitive Impairment to Alzheimer’s Disease: A Comparison of Tree-Based Machineents based on their risk grade. Moreover, we quantitatively compared their feature importance with Rank-Biased Overlap (RBO), a similarity measure between rankings. RSF showed highest .-index (0.87), followed by CSF and XST (0.85), while CPH had worst performance (0.83). According to RSF, CSF and XS作者: Genome 時間: 2025-3-27 02:14
https://doi.org/10.1007/b139104d a disentangled cosine distance loss is devised to ensure the disentanglement’s effectiveness. Moreover, the hierarchical representation fusion module is designed to effectively maximize the combination of relevant and effective features between modalities, which makes the generated structural-func作者: Retrieval 時間: 2025-3-27 09:15
https://doi.org/10.1007/b139104e sample covariance is used as an estimator of the ‘quasi-instantaneous’, brain activation pattern and derived from the multichannel signal recorded while the subject is gazing at a given product. Pattern derivation is followed by proper re-alignment to reduce covariate shift (inter-subject variabil作者: 搜尋 時間: 2025-3-27 10:59 作者: Mingle 時間: 2025-3-27 15:01
Tool 8: Intrakardiale Thrombusdiagnostik of redundant and synergystic information carried by these neurons about auditory stimuli. Our findings revealed that functionally connected pairs carry proportionally more redundancy and less synergy than unconnected pairs, suggesting that their functional connectivity is primarily redundant in nat作者: 箴言 時間: 2025-3-27 20:24
Kontrastmittel in der kardiovaskul?ren MRTion maps generated from fMRI transcoded from EEG and real fMRI data. The model also recovers the HRF and enables the interpretation of spatial and temporal patterns in the latent source space. Overall, our hierarchical deep transcoding model provides a valuable tool for integrating EEG and fMRI data作者: 同位素 時間: 2025-3-27 22:57
https://doi.org/10.1007/b139104 classification. The obtained difference was confirmed by testing the experiments using a method based on the Discrete Wavelet Transform (DWT) for feature extraction and classification using random forest. Using image-based stimulation may help to better understand low and high arousal/valence when 作者: 世俗 時間: 2025-3-28 03:22 作者: chronology 時間: 2025-3-28 07:13
Normale Anatomie und Anlagevarianten,ventional ESI methods in computer simulations. Our findings suggest that DeepSIF can provide accurate estimations of the source location and extent across various numbers of channels and noise levels, outperforming conventional methods, which indicates its merits for wide applications of ESI, especi作者: 六邊形 時間: 2025-3-28 14:10
https://doi.org/10.1007/3-540-32860-2erior efficacy of our proposed model in contrast to alternative techniques, as indicated by the SSIM and FID metrics. Moreover, our methodology has the potential to enhance the precision of Alzheimer’s disease assessment.作者: 表否定 時間: 2025-3-28 17:35 作者: 粗糙濫制 時間: 2025-3-28 20:45 作者: PARA 時間: 2025-3-29 01:59
Fusing Structural and?Functional Connectivities Using Disentangled VAE for?Detecting MCIormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this作者: flammable 時間: 2025-3-29 07:02 作者: 無節(jié)奏 時間: 2025-3-29 07:31 作者: 小卷發(fā) 時間: 2025-3-29 14:10
A Model of?the?Contribution of?Interneuron Diversity to?Recurrent Network Oscillation Generation andn focused on describing the encoding of information about external sensory stimuli carried by feed-forward inputs in a two-population circuit configuration that includes excitatory cells and fast-spiking interneurons. Here we extend these models to explore the contribution of different classes of co作者: 脆弱帶來 時間: 2025-3-29 16:22 作者: 毗鄰 時間: 2025-3-29 21:38
Fusing Simultaneously Acquired EEG and?fMRI via?Hierarchical Deep Transcodingesses in terms of spatial and temporal resolution. In this study, we propose a hierarchical deep transcoding model for fusing simultaneous EEG-fMRI data to recover a high spatiotemporal resolution latent neural source space. The model utilizes a cyclic Convolutional Neural Network (CNN) architecture作者: 彎曲道理 時間: 2025-3-30 02:08
Decoding Emotion Dimensions Arousal and?Valence Elicited on?EEG Responses to?Videos and?Images: A Coanikin (SAM) when subjects were exposed to videos or images. The classification is performed on electroencephalographic (EEG) signals from the DEAP public dataset, and a dataset collected at the University of Tsukuba, Japan. The experiments were defined to classify low versus high arousal/valence us作者: aptitude 時間: 2025-3-30 05:41 作者: expdient 時間: 2025-3-30 08:18 作者: 固執(zhí)點好 時間: 2025-3-30 14:47 作者: offense 時間: 2025-3-30 20:34 作者: 壕溝 時間: 2025-3-31 00:41 作者: Focus-Words 時間: 2025-3-31 00:51 作者: BURSA 時間: 2025-3-31 08:21
Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P30have been employed in different machine learning areas, are suitable for this type of classification. UNet (a convolutional neural network) is a classification algorithm proposed to improve the classification accuracy of P300 electroencephalogram (EEG) signals in a non-invasive brain-computer interf作者: 愛哭 時間: 2025-3-31 10:40 作者: 傻瓜 時間: 2025-3-31 17:11
Conversion from Mild Cognitive Impairment to Alzheimer’s Disease: A Comparison of Tree-Based Machinepproaches. However, typical ML classifiers are not able to provide information on time and risk to AD conversion. Survival Analysis statistical methods as Cox Proportional Hazard (CPH) give this information and can handle censored data, but they were designed for small dataset and do not perform wel作者: NEG 時間: 2025-3-31 21:36 作者: 昆蟲 時間: 2025-4-1 01:06
