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Titlebook: Brain Informatics; 15th International C Mufti Mahmud,Jing He,Ning Zhong Conference proceedings 2022 Springer Nature Switzerland AG 2022 art

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Toward the Study of the Neural-Underpinnings of Dyslexia During Final-Phoneme Elision: A Machine Leagruency components. It then uses a machine-learning algorithm to optimally combine the resulting components to differentiate between the neural activity of children with dyslexia and controls. We apply our approach to a real EEG dataset involving children with dyslexia and controls. Our findings dem
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Unstructured Categorization with Probabilistic Feedback: Learning Accuracy Versus Response Timeadopted by the observers; 2.) Accuracy and response time changed at a different rate during learning; 3.) The rate of improvement differed between the experiments; 4.) The response time is a better characteristic of incremental category learning. The findings imply that the learning performance depe
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Introducing the Rank-Biased Overlap as Similarity Measure for Feature Importance in Explainable Mach Imbalanced, undersampled (K-Medoids) and oversampled (SMOTE) datasets were used for training EBMs, obtaining their respective feature importance. RBO score was calculated between ranking pairs incrementally increasing the depth by five features, from 1 to 178. All classifiers reached excellent AUC-
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Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditationes. In addition, we generate lower-dimensional embeddings from higher-dimensional ones using t-SNE, PCA, and LLE algorithms and observe visual differences in embeddings between meditation and mind-wandering. We also discuss the general flow of the proposed design and contributions to the field of ne
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Enhancing the MR Neuroimaging by Using the Deep Super-Resolution Reconstructiondeep learning model; (2) bridging the 3T-MRI and the 7T-MRI within the same analysis scale; and (3) systematically comparing multiple evaluation indicators, including Brenner, SMD, SMD2, Variance, Vollath, Entropy, and NIQE. The experimental results suggest that the edge, fineness and texture featur
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Intracranial Space-Occupying Lesionst models, as well as employing an artefact detection model as a generic anomaly detector. Results show that subject-specific models can achieve a good performance, but the variability is significant across all three signals among rodents of the same age, gender and species.
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