Titlebook: Computational Neuroscience; 4th Latin American W Jaime A. Riascos Salas,Vinícius Rosa Cota,Daniel B Conference proceedings 2024 The Editor(

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https://doi.org/10.1007/978-3-662-39645-2r tasks, employing the Wilcoxon, Kruskal-Wallis, and Mann-Whitney statistic tests. Results showed a reduced dispersion of power spectral density during imagery. In addition, there were no statistically significant differences among imagery and actual movement execution.

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Application of the Epsilon-Greedy Algorithm for Frequency Optimization of Electrical Neurostimulatiut exceeding energy usage for the stimulation. Five different simulations were carried out in order to evaluate the contribution of the energy consumption in determining the minimum frequency. The results show the efficacy of the proposed algorithm to search the minimum pulse frequency necessary to suppress epileptic seizures.

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978-3-031-63847-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/d/image/242257.jpg

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https://doi.org/10.1007/978-3-662-39645-2ral network, initially proposed by Ronneberger et al. in 2015 for biomedical image segmentation, is described. Its performance is evaluated in the segmentation of human brain tumors utilizing authentic MRI data from the BraTS 2023 challenge. The specifics of the data augmentation algorithm and its i

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https://doi.org/10.1007/978-3-662-39645-2nomics, physiological computing, medical diagnostics or sport training. So far, the most commonly used machine learning algorithms to do so are linear classifiers such as Support Vector Machines (SVMs), often resulting in modest classification accuracies. However, Riemannian Geometry-based Classifie