Titlebook: Artificial Intelligence for Neuroscience and Emotional Systems; 10th International W José Manuel Ferrández Vicente,Mikel Val Calvo,Hojj Con

Levelheaded

書目名稱Artificial Intelligence for Neuroscience and Emotional Systems影響因子(影響力)




書目名稱Artificial Intelligence for Neuroscience and Emotional Systems影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence for Neuroscience and Emotional Systems網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence for Neuroscience and Emotional Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence for Neuroscience and Emotional Systems被引頻次




書目名稱Artificial Intelligence for Neuroscience and Emotional Systems被引頻次學(xué)科排名




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書目名稱Artificial Intelligence for Neuroscience and Emotional Systems年度引用學(xué)科排名




書目名稱Artificial Intelligence for Neuroscience and Emotional Systems讀者反饋




書目名稱Artificial Intelligence for Neuroscience and Emotional Systems讀者反饋學(xué)科排名

chisel

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

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

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

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

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

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

現(xiàn)代

影響

theta-waves

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