https://hdl.handle.net/11000/420 2026-04-03T17:27:02Z 2026-04-03T17:27:02Z Ortiz, Mario Vicente Vidal, Pablo Iáñez, Eduardo Montiel, Enrique Azorín, José M. https://hdl.handle.net/11000/39683 2026-03-31T07:10:30Z 2026-03-30T15:12:13Z

Título : Assessing Footwear Comfort by Electroencephalography Analysis Autor : Ortiz, Mario; Vicente Vidal, Pablo; Iáñez, Eduardo; Montiel, Enrique; Azorín, José M. Resumen : Footwear comfort is one of the determinant factors in a buyout decision. The understanding of which brain patterns are involved in the comfort perception of footwear could be an important element to develop the consumer neuroscience field, and could even help during the development phase of new products. The present paper studies the comfort perception through the electroencephalography analysis of the brain signals of ten subjects during walking. For the analysis, different features were extracted from the subject’s biosignals based on power spectral density attributes and temporal and statistical parameters of the data under analysis. The research compared the features when the subjects were wearing a comfortable and a uncomfortable shoe by size on a treadmill. The results indicate that both kind of shoes could be classified with average accuracies of 84,3% and that an influence of parietal, tempo-parietal and in a minor way frontal lobes was detected. Despite the subject’s dependency on the results, the research demonstrates that a common electrode and feature configuration could be applied keeping the results in an average accuracy of 83,7% and that a reduction to a 12 electrode setup maintains the accuracy at a 78,0% value.

2026-03-30T15:12:13Z Ortiz, Mario de la Ossa, Luis Juan Poveda, Javier V. Iáñez, Eduardo Torricelli, Diego Tornero, Jesús Azorín, José M. https://hdl.handle.net/11000/39682 2026-03-31T07:10:30Z 2026-03-30T15:11:20Z

Título : An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton Autor : Ortiz, Mario; de la Ossa, Luis; Juan Poveda, Javier V.; Iáñez, Eduardo; Torricelli, Diego; Tornero, Jesús; Azorín, José M. Resumen : One important point in the development of a brain-machine Interface (BMI) commanding an exoskeleton is the assessment of the cognitive engagement of the subject during the motor imagery tasks conducted. However, there are not many databases that provide electroencephalography (EEG) data during the use of a lower-limb exoskeleton. The current paper presents a database designed with an experimental protocol aiming to assess not only motor imagery during the control of the device, but also the attention to gait on flat and inclined surfaces. The research was conducted as an EUROBENCH subproject in the facilities sited in Hospital Los Madroños, Brunete (Madrid). The data validation reaches accuracies over 70% in the assessment of motor imagery and attention to gait, which marks the present database as a valuable resource for researches interested on developing and testing new EEG-based BMIs.

2026-03-30T15:11:20Z Gracia Laso, Desirée Irene Ortiz, Mario Candela, Tatiana Iáñez, Eduardo Sánchez, Rosa M. Díaz Marín, Carmen Azorín, José M. https://hdl.handle.net/11000/39681 2026-03-31T07:10:30Z 2026-03-30T15:10:15Z

Título : Design and Evaluation of a Potential Non-Invasive Neurostimulation Strategy for Treating Persistent Anosmia in Post-COVID-19 Patients Autor : Gracia Laso, Desirée Irene; Ortiz, Mario; Candela, Tatiana; Iáñez, Eduardo; Sánchez, Rosa M.; Díaz Marín, Carmen; Azorín, José M. Resumen : A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world’s population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection.

2026-03-30T15:10:15Z Juan, Javier V. Martínez Sánchez De La Torre, Rubén Iáñez, Eduardo Ortiz, Mario Tornero, Jesús Azorín, Jose M. https://hdl.handle.net/11000/39640 2026-03-31T01:07:44Z 2026-03-30T07:50:23Z

Título : Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet Autor : Juan, Javier V.; Martínez Sánchez De La Torre, Rubén; Iáñez, Eduardo; Ortiz, Mario; Tornero, Jesús; Azorín, Jose M. Resumen : Introduction: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery.Methods: This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction.Results and discussion: To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

2026-03-30T07:50:23Z Ferrero, Laura Soriano Segura, Paula Navarro, Jacobo Jones, Oscar Ortiz, Mario Iáñez, Eduardo Azorín, José M. Contreras Vidal, José L. https://hdl.handle.net/11000/39639 2026-03-31T01:07:54Z 2026-03-30T07:49:28Z

Título : Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study Autor : Ferrero, Laura; Soriano Segura, Paula; Navarro, Jacobo; Jones, Oscar; Ortiz, Mario; Iáñez, Eduardo; Azorín, José M.; Contreras Vidal, José L. Resumen : Background This research focused on the development of a motor imagery (MI) based brain–machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. Methods A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants’ neural activity using the second deep learning approach for the decoding. Results The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. Conclusion This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study’s discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

2026-03-30T07:49:28Z Soriano Segura, Paula Ortiz, Mario Iáñez, Eduardo Azorín, Jose M. https://hdl.handle.net/11000/39638 2026-03-30T08:09:25Z 2026-03-27T19:38:43Z

Título : Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential Autor : Soriano Segura, Paula; Ortiz, Mario; Iáñez, Eduardo; Azorín, Jose M. Resumen : Background and objective Brain-Machine Interfaces (BMIs) based on a motor imagination paradigm provide an intuitive approach for the exoskeleton control during gait. However, their clinical applicability remains difficulted by accuracy limitations and sensitivity to false activations. A proposed improvement involves integrating the BMI with methods based on detecting Error Related Potentials (ErrP) to self-tune erroneous commands and enhance not only the system accuracy, but also its usability. The aim of the current research is to characterize the ErrP at the beginning of the gait with a lower limb exoskeleton to reduce the false starts in the BMI system. Furthermore, this study is valuable for determining which type of feedback, Tactile, Visual, or Visuo-Tactile, achieves the best performance in evoking and detecting the ErrP. Methods The initial phase of the research concentrates on detecting ErrP at the beginning of gait to improve the efficiency of an asynchronous BMI based on motor imagery (BMI-MI) to control a lower limb exoskeleton. Initially, an experimental protocol is designed to evoke ErrP at the start of gait, employing three different stimuli: Tactile, Visual, and Visuo-Tactile. An iterative selection process is then utilized to characterize ErrP in both time and frequency domains and fine-tune various parameters, including electrode distribution, feature combinations, and classifiers. A generic classifier with 6 subjects is employed to configure an ensemble classification system for detecting ErrP and reducing the false starts. Results The ensembled system configured with the selected parameters yields an average correction of false starts of 72.60 % ± 10.23, highlighting its corrective efficacy. Tactile feedback emerges as the most effective stimulus, outperforming Visual and Visuo-Tactile feedback in both training types. Conclusions The results suggest promising prospects for reducing the false starts when integrating ErrP with BMI-MI, employing Tactile feedback. Consequently, the security of the system is enhanced. Subsequent, further research efforts will focus on detecting error potential during movement for gait stop, in order to limit undesired stops.

2026-03-27T19:38:43Z Polo Hortigüela, Cristina Ortiz, Mario Soriano Segura, Paula Iáñez, Eduardo Azorín, José M. https://hdl.handle.net/11000/39637 2026-03-30T08:09:25Z 2026-03-27T19:35:33Z

Título : Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton Autor : Polo Hortigüela, Cristina; Ortiz, Mario; Soriano Segura, Paula; Iáñez, Eduardo; Azorín, José M. Resumen : Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain–machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert–Huang (HHT), and Chirplet (CT) methods. The 8–20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.

2026-03-27T19:35:33Z Soriano Segura, Paula Ortiz, Mario Polo Hortigüela, Cristina Iáñez, Eduardo Azorín, José M. https://hdl.handle.net/11000/39636 2026-03-30T08:09:25Z 2026-03-27T19:31:28Z

Título : Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning Autor : Soriano Segura, Paula; Ortiz, Mario; Polo Hortigüela, Cristina; Iáñez, Eduardo; Azorín, José M. Resumen : Background Brain-Machine Interfaces (BMI) based on motor imagery (MI) are promising assistive neurotechnology tools for gait rehabilitation that allow users to control exoskeletons by imagining motor actions. Literature has proven the influence of BMIs over neuroplasticity mechanisms. However, the accuracy of MI-BMIs is often limited by the weak brain signals associated with lower-limb movements. To enhance system reliability and safety, Error-Related Potentials (ErrP), which are exogenous potentials evoked by erroneous system actions, can be integrated to correct commands. This study characterizes and detects ErrP during the use of a lower-limb exoskeleton, making use of a deep learning approach to improve accuracy and robustness over traditional classifiers. Methods ErrP detection is performed using the EEG-Inception neural network, a convolutional deep learning model, and applying data augmentation techniques to the imbalanced dataset. The methodology is tested first for the characterization of ErrP during the start of gait with static data and, after confirming its improvement, regarding previous developments, it is also applied to motion data during the stop of the exoskeleton. With this objective, an experimental protocol is designed to evoke ErrP and NoErrP during motion, using tactile stimuli. ErrP is elicited when the exoskeleton stops erroneously in a gait region, while NoErrP is generated when it stops correctly in a stop region. Results The proposed approach achieves a True Positive Rate (TPR) of approximately 95% and a False Positive Rate (FPR) below 20% in both static and motion conditions, significantly outperforming traditional ensemble classifiers. In terms of MI-BMI performance, these results indicate that most erroneous commands are successfully canceled, while only a small number of correct commands are wrongly canceled. In addition, statistical analysis revealed no significant differences between the detection of ErrP in static and motion scenarios, nor between sessions or subjects just in static. However, significant differences are observed between subjects in motion and also the outcomes of ErrP and NoErrP classes in both scenarios. Conclusion The EEG-Inception neural network provides a robust and accurate method for ErrP detection. Future research will focus on integrating ErrP detection with MI classifiers and validating the system with SCI patients for improved gait rehabilitation therapies.

2026-03-27T19:31:28Z Cuadrado-Sempere, Óscar Fabra-Rodríguez, Miguel Navarro-Arcas, Abel Riquelme Simón-Portillo, Francisco Javier Sánchez-Lozano, Miguel Velasco-Sánchez, Emilio https://hdl.handle.net/11000/39335 2026-02-17T02:08:20Z 2026-02-16T17:50:45Z

Título : New Procedure Based on Obtaining the Average Deceleration for Improving the Spanish National Inspection of Tractor Service Brakes Autor : Cuadrado-Sempere, Óscar; Fabra-Rodríguez, Miguel; Navarro-Arcas, Abel Riquelme; Simón-Portillo, Francisco Javier; Sánchez-Lozano, Miguel; Velasco-Sánchez, Emilio Resumen : This study addresses the status and proposes key aspects relevant to refining of the procedure for the Spanish National Inspection of Vehicles relative to tractor service brakes, specifically focusing on obtaining the average deceleration. The current inspection method lacks a clear definition of the calculation process for average deceleration in the Spanish Official Manual of Procedures of Inspection of Vehicles. This lack of clarity results in varying outcomes and potential acceptance or rejection of the inspection based on the selected equipment and procedure. To address this issue, a comprehensive series of tests were conducted on different vehicles, using various equipment and calculation methods. The objective was to derive meaningful conclusions regarding the calculation method, measuring equipment, and other relevant factors. This study reveals the inadequacy of the current average deceleration calculation method as outlined in the official inspection manual. The findings highlight the crucial role of accurate equipment selection, appropriate calculation methods, and skilled personnel experience in ensuring reliable and consistent results. To address this, a new standardized procedure is proposed to streamline the process of obtaining the average deceleration in the inspection of tractor service brakes. The recommended procedure encompasses the use of a secure, well-defined track, clearly marked acceleration and braking points, a GPS decelerometer, a portable or on-track speedometer, and data processing that excludes the initial and final sections of the deceleration curve. Furthermore, this study highlights the need to update the acceptance thresholds for the inspection, as the current thresholds may no longer align with the proposed procedure. A revision of these thresholds is suggested to establish new criteria that are more appropriate and in line with the proposed method and for tractors manufactured after 01/01/2016.

2026-02-16T17:50:45Z Cuadrado-Sempere, Óscar Simón-Portillo, Francisco Javier Ruzafa-Silvestre, Carlos Orgiles-Calpena, Elena Arán-Aís, Francisca Martins da Silva, Lucas Filipe Sánchez-Lozano, Miguel https://hdl.handle.net/11000/39334 2026-02-17T02:08:19Z 2026-02-16T17:49:47Z

Título : Enhanced adhesion properties of hyperelastic adhesives to different nature substrates by applying different surface treatments Autor : Cuadrado-Sempere, Óscar; Simón-Portillo, Francisco Javier; Ruzafa-Silvestre, Carlos; Orgiles-Calpena, Elena; Arán-Aís, Francisca; Martins da Silva, Lucas Filipe; Sánchez-Lozano, Miguel Resumen : This study investigates the effect of different surface treatments on the bond strength of hyperelastic adhesive joints. Plasma treatment and primer application were compared to sandblasting and sandblasting plus priming on aluminium and Glass-fibre Reinforced Polyester (GRP) surfaces. Results indicated that plasma treatment on aluminium surfaces increased shear strength values by 20 % compared to untreated surfaces and showed a shear strength comparable to primer application. However, on GRP surfaces, primer application improved bond strength by 10 %, while plasma treatment did not provide any significant enhancement. Plasma and primer treatments produce predominantly cohesive joint failure, while sandblasting and sandblasting plus priming produce large percentage of adhesive failure. Plasma treatment may serve as a good alternative to primer on aluminium surfaces, while primer remains preferred for GRP surfaces. The study concludes that the dispersive contact angle has a direct relationship with shear strength, where a 50 % decrease in dispersive contact angle results in a 10 % increase in shear strength. This suggests that substrate surfaces with a higher affinity for dispersive compounds would form stronger bonds.

2026-02-16T17:49:47Z