Enrique Hortal

Enrique Hortal
Maastricht University | UM · Department of Advanced Computing Sciences

PhD in Industrial and telecommunications technologies

About

57
Publications
11,862
Reads
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620
Citations
Citations since 2017
20 Research Items
518 Citations
2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
2017201820192020202120222023020406080100
Additional affiliations
August 2019 - present
Maastricht University
Position
  • Professor (Assistant)
Description
  • - Teaching topics: Computer Science, Machine Learning, Affective computing, Human-Computer Interaction. - Coordinator of the Bachelor Thesis programme Research topics: Signal processing, Affective computing
April 2016 - July 2019
Maastricht University
Position
  • PostDoc Position
Description
  • Coordination activities and co-supervision of two PhD candidates in the context of the European project MaTHiSiS (Horizon2020-ICT-20-2015). Teaching responsibilities: Discrete Mathematics, Computer Sciences and Project coordination.
October 2013 - March 2016
Universidad Miguel Hernández de Elche
Position
  • Researcher
Education
September 2012 - February 2016
Universidad Miguel Hernández de Elche
Field of study
  • PhD in Industrial and telecommunications technologies
September 2011 - October 2012
Universidad Miguel Hernández de Elche
Field of study
  • Master Degree in Industrial and telecommunications technologies
September 2003 - February 2009
Universidad Miguel Hernández de Elche
Field of study
  • Bachelor degree in Telecommunications engineering, specialized in electronic systems

Publications

Publications (57)
Conference Paper
Artificial neural networks are inspired by information processing performed by neural circuits in biology. While existing models are sufficient to solve many real-world tasks, they are far from reaching the potential of biological neural networks. These models are oversimplifications of their biological counterparts, omitting key features such as t...
Preprint
Full-text available
Speech synthesis is used in a wide variety of industries. Nonetheless, it always sounds flat or robotic. The state of the art methods that allow for prosody control are very cumbersome to use and do not allow easy tuning. To tackle some of these drawbacks, in this work we target the implementation of a text-to-speech model where the inferred speech...
Article
Full-text available
Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive...
Article
Full-text available
Currently, in all augmented reality (AR) or virtual reality (VR) educational experiences, the evolution of the experience (game, exercise or other) and the assessment of the user’s performance are based on her/his (re)actions which are continuously traced/sensed. In this paper, we propose the exploitation of the sensors available in the AR/VR syste...
Article
Full-text available
Accessing large, manually annotated audio databases in an effort to create robust models for emotion recognition is a notably difficult task, handicapped by the annotation cost and label ambiguities. On the contrary, there are plenty of publicly available datasets for emotion recognition which are based on facial expressivity due to the prevailing...
Article
The use of recommendation systems has been extensively applied to different fields from the suggestion of multimedia content to other areas such as education. Thereof, the integration of affect-related information becomes a key factor to enhance the user experience and, when it comes to learning, it can be translated into the maximization of knowle...
Chapter
This chapter describes the use of a multimodal system designed with a view to controlling the movements of an assistive robotic system. The multimodal control is based on two biosignals, namely electrooculography and electroencephalography. The chapter includes a description of the electrooculogram-based system along with the brain-machine interfac...
Chapter
This chapter presents the initial works developed in the context of this thesis regarding the analysis of the brain activity based on motor imagery techniques. The chapter starts with a description of the phenomenon under study followed by the methodology applied in order to distinguish between different mental tasks. In this work, methods to infer...
Chapter
This chapter joints the main methodologies presented in previous chapters with a view to enabling an efficient rehabilitation system managed by the users through their mental activity. The chapter presents the experiments conducted by patients suffering from motor disabilities and non-diagnosed users and the results obtained are compared. The chapt...
Chapter
The second method utilized for the analysis of brain signals developed in this thesis is described in this chapter. This section includes a description of the principal brain signal’s potentials utilized with the purpose of detecting the mental activity of an individual. Furthermore, a methodology to detect users’ movement intention through the ana...
Conference Paper
This paper introduces an end-to-end solution for dynamic adaptation of the learning experience for learners of different personal needs, based on their behavioural and affective reaction to the learning activities. Personal needs refer to what learner already know, what they need to learn, their intellectual and physical capacities and their learni...
Chapter
In this work, three different cognitive mechanisms were analyzed during gait. First, a real-time index of attention level was obtained in real-time. The detection of starting and stopping indices was also evaluated. Finally, the detection of obstacle appearance allows increasing safety during experiments providing a stop command when necessary. The...
Conference Paper
Full-text available
The growing prevalence of Internet during the last decades has made e-learning systems and Computer-based Education (CBE) widely accessible to a great amount of people with different backgrounds and competences. Due to these rapid advances in computer technologies, there has been a great shift from conventional, low interaction and printed learning...
Chapter
In this paper, an experiment designed to detect the will to perform several steps forward (as gait initiation) before it occurs using the electroencephalographic (EEG) signals collected from the scalp is presented. In order to detect this movement intention, the Event-Related Desynchronization phenomenon is detected using a SVM-based classifier. Th...
Conference Paper
Recovery from cerebrovascular accident (CVA) is a growing research topic. Exoskeletons are being used for this purpose in combination with a volitional control algorithm. This work studied the intention of pedaling initiation movement, based on previous work, with different types of electrode configuration and different processing time windows. The...
Article
Full-text available
Rehabilitation techniques are evolving focused on improving their performance in terms of duration and level of recovery. Current studies encourage the patient's involvement in their rehabilitation. Brain-Computer Interfaces are capable of decoding the cognitive state of users to provide feedback to an external device. On this paper, cortical infor...
Article
Walking is for humans an essential task in our daily life. However, there is a huge (and growing) number of people who have this ability diminished or are not able to walk due to motor disabilities. In this paper, a system to detect the start and the stop of the gait through electroencephalographic signals has been developed. The system has been de...
Chapter
Full-text available
Este capítulo se centra en las interfaces hombre-máquina empleadas en cada exoesqueleto, describiendo las modalidades de interacción consideradas, los algoritmos implementados y como se lleva a cabo la validación del funcionamiento de las interfaces.
Article
Full-text available
Background: When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) si...
Article
Full-text available
Background As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes. Methods In this work, a s...
Article
Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters when linear regression decoding models are applied to low frequency EEG components. However, the decoding performance is far from being sufficient to obtain an accurate control. In this paper, we propose the use of th...
Conference Paper
Full-text available
Brain Machine Interface (BMI) and Software Agent (SA) can provide some new adaptive strategies for robust BMI implementations. In this work, a non-invasive Adaptive BMI is introduced, which has been designed to discriminate four mental tasks. The SA allows tracking features to contribute for an adaptive process, while the user's engagement state pr...
Article
This paper presents a multimodal Human–Machine Interface system that combines an Electrooculography Interface and a Brain–Machine Interface. This multimodal interface has been used to control a robotic arm to perform pick and place tasks in a three dimensional environment. Five volunteers were asked to pick two boxes and place them in different pos...
Conference Paper
The ability of walking brings us a great freedom in our daily life. However, there is a huge number of people who have this ability diminished or are not even able to walk due to motor disabilities. This paper presents a method to detect the voluntary initiation and stop of the gait cycle using the ERD phenomenon. The system developed obtains a goo...
Article
Full-text available
The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG)...
Article
Human-Machine Interfaces can be very useful to improve the quality of life of physically impaired users. In this work, a non-invasive spontaneous Brain-Machine Interface (BMI) has been designed to control a robot arm through the mental activity of the users. This BMI uses the classification of four mental tasks in order to manage the movements of t...
Conference Paper
Full-text available
A suitable selection of feature depends on the performance of a Brain Computer Interface (BCI), reducing the large cross-validation process. This work proposes the use of Silhouette’s width as a performance index to evaluate which features provide the best performance (accuracy) in mental task for BCI implementations. Four mental tasks are used to...
Chapter
Motor disability may be caused by many different conditions. The most common one is a cerebrovascular accident (CVA) which occurs when the blood supply to the brain stops [1]. If the length of this interruption is longer than several seconds, brain cells can die causing a permanent damage in the patient. When this damage occurs in the brain areas r...
Article
Full-text available
Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large tr...
Article
Full-text available
This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement...
Conference Paper
In this work, a study that analyzes the best combinations of mental tasks in a Brain-Computer Interface (BCI) using a classifier based on Support Vector Machine (SVM) is presented. To that end, 12 mental tasks of different nature are analyzed and the results of the classification for the combinations of two, three and four tasks are obtained. Four...
Article
Recent studies have hypothesized that the motor cortex is particularly active during specific phases of gait cycle. It has been found that cortical coherence appearance differs in time depending on walking speed. In this work, we analyze the influence of walking speed by decoding knee angles from low frequency EEG components. Linear regression mode...
Article
In this paper, a non-invasive spontaneous Brain-Machine Interface (BMI) is used to control the movement of a planar robot. To that end, two mental tasks are used to manage the visual interface that controls the robot. The robot used is a PupArm, a force-controlled planar robot designed by the nBio research group at the Miguel Hernández University o...
Conference Paper
The ability to walk is a very important characteristic of the human being that unfortunately not everyone can enjoy. Subjects with spinal cord damage or who have had a stroke may have difficulties in walking, or even be unable to walk, depending on their degree of disability. Although, in some cases, it is possible to recover mobility, the rehabili...
Conference Paper
The large amount of patients suffering from motor disabilities has motivated a lot of studies in order to improve their mobility and quality of life. A Brain-Machine Interface (BMI) can be very useful to control a system that is able to improve the independence of people with motor disabilities. The electroencephalographic (EEG) signals are commonl...
Chapter
The goal of this paper is to detect error-related EEG potential (ErrP) to perform lower limb rehabilitation tasks. The detection of this potential can be used as a support mechanism to avoid error during this rehabilitation. For that purpose, a graphical interface has been used to simulate the error in the movement of a cursor that generate the app...
Article
In this paper is presented an experiment designed to detect the will to perform several steps forward (as walking onset) before it occurs using the electroencephalographic (EEG) signals collected from the scalp. The preliminary results from five users have been presented. In order to improve the quality of the signals acquired some different spatia...
Chapter
In the world there is a large number of people who have trouble performing movements that are simple for others, such as people who have suffered a stroke or have damage in the spinal cord. However, thanks to neuroscience, there is knowledge about the cognitive processes that occur in the brain and it is possible to help these people by using brain...
Chapter
The combination of Brain-Machine Interfaces (BMIs) with assistive technologies has seen a rapid development during the last few years. Regarding post-stroke rehabilitation, the BMI could be combined with other sensors to assist movements performed by the patient by attaching an exoskeleton to the affected arm. To that end, the patient’s arm movemen...
Conference Paper
A Brain-Computer Interface (BCI) can be very useful to help people with several movement disabilities to improve their independence or to assist them in rehabilitation tasks. In this paper, the results of the online classification of two mental tasks from electroencephalographic signals (EEG) are shown. The objective of this paper is to determine w...
Conference Paper
The past decade has seen the rapid development of upper limb kinematic decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Previous works show that there is a correlation between electroencephalographic signals a...
Conference Paper
Support Vector Machine (SVM) is extensively used in BCI classification. In this paper this classifier is used to differentiate between two mental tasks related to motor imaginary in order to check the possibility of improvement with two alternative adaptation (user’s adaptation and model adaptation). Two kind of training have been done by 4 subject...
Conference Paper
This paper studies the ergonomic issues of a new multimodal application based on the combination of an electrooculography interface and a brain-computer interface to interact with a robot arm. This application allows performing pick and place operations in a specific designed environment. Eight able-bodied volunteers tested the application and were...
Conference Paper
In this work, a study that analyzes the best combinations of mental tasks in a Brain-Computer Interface (BCI) using a classifier based on Support Vector Machine (SVM) is presented. To that end, twelve mental tasks of different nature are analyzed and the results of the classification for the combinations of two, three and four tasks are obtained. F...
Conference Paper
In this paper, a visual interface showing target selection tasks has been designed to decode 2D velocities from a brain-computer interface (BCI) system during hand movements. Ten healthy volunteers were asked to perform hand movements using a mouse to control a cursor on the screen. Three targets were randomly highlighted and the volunteers were as...
Chapter
The main goal of this paper is decoding the movement velocity of the upper limb to be applied in a future rehabilitation procedures in patients suffering from a stroke. For that purpose, the one-dimensional upper limb movements have been reconstructed by analyzing information from electroencephalographic signals (EEG). The EEG signals were recorded...

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Projects (2)
Project
JOINclusion is intended to foster the social inclusion of young immigrants (primary school students) and with a particular focus on refugees through the use of ICT tools and more specifically a collaborative mobile application. This tool will be designed by psychologists specializing in the field with the aim of developing empathy learning scenarios strengthening the impact of their use. The scenarios will be designed to promote dialogue between participants and facilitate channels to express themselves, promoting integration. Both the game and its scenario will be designed based on schools needs, involving end-users (not only teachers and educators but also students) in the early stages of the project development. Furthermore, the collaborative serious game will be boosted by machine learning techniques conceived to enhance user experience and optimize its efficiency as a tool promoting prosociality through personalisation based on affect detection. The interactions with the collaborative serious game will be also designed to encourage self-reflection and post-game interaction among the members of the targeted groups (children with and without a migrant background) while, for children at risk of exclusion, it will promote reassurance to encourage them to learn to act as equal members of their new community and find their place in society. Website: https://erasmus-plus.ec.europa.eu/projects/search/details/2021-2-NL01-KA220-SCH-000048865