Examinando por Autor "Fernandez De Retana Uribe, Miguel"

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    Analysing the impact of images and text for predicting human creativity through encoders
    (Science and Technology Publications, Lda, 2025) Pikatza Huerga, Amaia; Matanzas de Luis, Pablo; Fernandez De Retana Uribe, Miguel; Peña Lasa, Javier; Zulaika Zurimendi, Unai; Almeida, Aitor
    This study explores the application of multimodal machine learning techniques to evaluate the originality and complexity of drawings. Traditional approaches in creativity assessment have primarily focused on visual analysis, often neglecting the potential insights derived from accompanying textual descriptions. The research assesses four target features: drawings’ originality, flexibility and elaboration level, and titles’ creativity, all labelled by expert psychologists. The research compares different image encoding and text embeddings to examine the effectiveness and impact of individual and combined modalities. The results indicate that incorporating textual information enhances the predictive accuracy for all features, suggesting that text provides valuable contextual insights that images alone may overlook. This work demonstrates the importance of a multimodal approach in creativity assessment, paving the way for more comprehensive and nuanced evaluations of artistic expression.
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    A deep learning approach to artifact removal in Transcranial Electrical Stimulation: from shallow methods to deep neural networks and state space models
    (Elsevier Ltd, 2025-11-19) Fernandez De Retana Uribe, Miguel; Matanzas de Luis, Pablo; Peña Lasa, Javier; Almeida, Aitor
    Transcranial Electrical Stimulation (tES) is a non-invasive neuromodulation technique that generates artifacts in simultaneous EEG recordings, hindering brain activity analysis. This study analyzes Machine Learning (ML) methods for tES noise artifact removal across three stimulation types: tDCS, tACS, and tRNS. Synthetic datasets were created by combining clean EEG data with synthetic tES artifacts. Eleven artifact removal techniques were tested and evaluated using the Root Relative Mean Squared Error (RRMSE) in the temporal and spectral domains, and the Correlation Coefficient (CC). Results indicate that method performance is highly dependent on stimulation type: for tDCS, a convolutional network (Complex CNN) performed best; while a multi-modular network (M4) based on State Space Models (SSMs) yielded the best results for tACS and tRNS. This study provides guidelines for selecting efficient artifact removal methods for different tES modalities, establishing a benchmark for future research in this area and paving the way for more robust analysis of neural dynamics in advanced clinical and neuroimaging applications.