Levelheaded
发表于 2025-3-21 16:33:14
书目名称Artificial Intelligence for Neuroscience and Emotional Systems影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0162387<br><br> <br><br>书目名称Artificial Intelligence for Neuroscience and Emotional Systems读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0162387<br><br> <br><br>
chisel
发表于 2025-3-21 21:06:55
Visualizing Brain Synchronization: An Explainable Representation of Phase-Amplitude Coupling various cognitive processes and constitutes the basis of communication between populations of neurons. Cross-frequency coupling (CFC) refers to techniques directed to study the interactions between oscillations at different frequencies, providing a more comprehensive view of neural dynamics than tr
Crater
发表于 2025-3-22 03:52:37
Enhancing Neuronal Coupling Estimation by NIRS/EEG Integrationr diagnosis. In this context, multimodal neuroimaging approaches, based on the neurovascular coupling phenomenon, exploit their individual strengths to provide complementary information on the neural activity of the brain cortex. This work proposes a novel method for combining electroencephalography
BOOR
发表于 2025-3-22 04:40:56
Causal Mechanisms of Dyslexia via Connectogram Modeling of Phase Synchronyion flow between different brain regions. Connectograms are graphical representations that map the connectivity between neural nodes or EEG channels through lines and arrows of varying thickness and directionality. Here, inter-channel phase connectivity patterns were analyzed by computing Granger ca
Mundane
发表于 2025-3-22 08:49:45
Explainable Exploration of the Interplay Between HRV Features and EEG Local Connectivity Patterns instem on the heart. It can provide insights into the balance between sympathetic and parasympathetic activity. The relationship between autonomic nervous system function, specifically parasympathetic activity, and certain learning disorders, including dyslexia, is currently under study. In this paper
candle
发表于 2025-3-22 16:30:19
Enhancing Intensity Differences in EEG Cross-Frequency Coupling Maps for Dyslexia DetectionFrequency Coupling (CFC) maps derived from EEG signals for dyslexia detection. Our approach addresses the challenge of subtle intensity differences in CFC maps, which can hinder the accurate identification of dyslexia-related patterns..Through visual inspection and quantitative analysis, we demonstr
格子架
发表于 2025-3-22 20:02:04
Improving Prediction of Mortality in ICU via Fusion of SelectKBest with SMOTE Method and Extra Tree chanism based on Artificial Intelligence (AI), utilizing technology to explore hidden relations between data and assessment in medical contexts. Hence, predicting the mortality of Intensive Care Unit (ICU) patients is a vital yet challenging task with significant implications for clinical decision-m
现代
发表于 2025-3-22 23:59:48
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影响
发表于 2025-3-23 03:43:39
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theta-waves
发表于 2025-3-23 08:22:27
Enhancing Interpretability in Machine Learning: A Focus on Genetic Network Programming, Its Variants processes. However, the interpretability of solutions generated by these algorithms remains a significant challenge, as these models do not inherently prioritize explainability. This lack of interpretability hampers the analysis of decision-making rationales. One potential remedy to this issue is t