Del-Valle-Soto, Carolina
Main Affiliation
Preferred name
Del-Valle-Soto, Carolina
Official Name
del Valle Soto, Carolina
ORCID
0000-0002-0272-3275
Researcher ID
T-5779-2017
Scopus Author ID
56257456700
121 results
Now showing 1 - 10 of 121
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Item type:Publication, Real-Time Object Finding for the Visually Impaired Using an Image-to-Speech Wearable Device(Springer Nature Switzerland, 2025-11-18); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A transformer-based method for radio-frequency fingerprinting of IoT devices(Elsevier BV, 2026-04); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Generative AI and the scientific landscape: a bibliometric exploration of its global impact(Editorial Académica Dragón Azteca, 2026-02-16) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forehead and In-Ear EEG Acquisition and Processing: Biomarker Analysis and Memory-Efficient Deep Learning Algorithm for Sleep Staging with Optimized Feature Dimensionality(MDPI AG, 2025-10-01); Massimo De VittorioAdvancements in electroencephalography (EEG) technology and feature extraction methods have paved the way for wearable, non-invasive systems that enable continuous sleep monitoring outside clinical environments. This study presents the development and evaluation of an EEG-based acquisition system for sleep staging, which can be adapted for wearable applications. The system utilizes a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board to acquire EEG signals from the forehead and in-ear regions under various conditions, including visual and auditory stimuli. Afterward, the acquired signals were processed to extract a wide range of features in time, frequency, and non-linear domains, selected based on their physiological relevance to sleep stages and disorders. The feature set was reduced using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and Principal Component Analysis (PCA), resulting in a compact and informative subset of principal components. Experiments were conducted on the Bitbrain Open Access Sleep (BOAS) dataset to validate the selected features and assess their robustness across subjects. The feature set extracted from a single EEG frontal derivation (F4-F3) was then used to train and test a two-step deep learning model that combines Long Short-Term Memory (LSTM) and dense layers for 5-class sleep stage classification, utilizing attention and augmentation mechanisms to mitigate the natural imbalance of the feature set. The results—overall accuracies of 93.5% and 94.7% using the reduced feature sets (94% and 98% cumulative explained variance, respectively) and 97.9% using the complete feature set—demonstrate the feasibility of obtaining a reliable classification using a single EEG derivation, mainly for unobtrusive, home-based sleep monitoring systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis(MDPI AG, 2026-01-18); ; ; - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games(MDPI AG, 2025-10-29); ; ; ; Francisco R. Castillo-SoriaThis study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Systematic Review and Energy-Centric Taxonomy of Jamming Attacks and Countermeasures in Wireless Sensor Networks(MDPI AG, 2026-01-15); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Efficient Deep Learning-Based M-PSK Detection for OFDM V2V Systems Using MobileNetV3(MDPI AG, 2026-03-11); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effects of a dual intervention (motor and virtual reality-based cognitive) on cognition in patients with mild cognitive impairment: a single-blind, randomized controlled trial(2024); Scopus© Citations 3 5