This book constitutes the refereed proceedings of the 5th Latin American Workshop, LAWCN 2025, held in La Plata, Argentina, during November 12–14, 2025.
The 6 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 11 submissions. They were organized in the follwing topical sections as follows: Artificial Intelligence and Machine Learning; and Neuroengineering.
Edited by:
Alan Talevi, Vinicius Rosa Cota Imprint: Springer Nature Switzerland AG Country of Publication: Switzerland Dimensions:
Height: 235mm,
Width: 155mm,
ISBN:9783032146632 ISBN 10: 3032146631 Series:Communications in Computer and Information Science Pages: 123 Publication Date:29 January 2026 Audience:
College/higher education
,
Professional and scholarly
,
Further / Higher Education
,
Undergraduate
Format:Paperback Publisher's Status: Active
.- Artificial Intelligence and Machine Learning. .- Multimodal Physiological Signal Statistical Analysis and Random Forest Classification for Epileptic Seizure Detection. .- Detection of Epileptogenic Patterns in Thalamic Neurostimulated Signals through Spatial Deep Attention. .- In Silico Screening to Identify Metabotropic Glutamate Receptor 1 Allosteric Modulators Using Ensemble Learning. .- A Comparative Analysis of ANN, TabNet and FT-Transformer models in EEG Classification of Neuropsychiatric Disorders. .- Computational and Experimental Approaches for the Discovery of New Anticonvulsant Drugs. .- Application of Machine Learning in Drug Repurposing of a New Antiseizure Drugs Active in the PTZ Kindling Model. .- Neuroengineering. .- Design and Development of a Functional Prototype of an Upper-Limb Exoskeleton for Neurorehabilitation Assistance. .- An Inverted Perspective for the Reference Electrode and a Heuristic Metric for Spectra-Temporal Mapping of EEG Signals Aiming at Motor-Imagery Classification. .- Beyond Full Electrode Arrays: Optimizing Channel Selection for Motor Imagery BCIs via Individual Channel Classifiers and Ensembles. .- Hemispheric-Specific Coupling Improves Modeling of Functional Connectivity Using Wilson–Cowan Dynamics.