Mario Ortiz García

• Doctor of Engineering
• Miguel Hernández University of Elche

La presente investigación tiene como objetivo la detección del Potencial de Error (ErrP) en movimiento que se produce al detener erróneamente un exoesqueleto de miembro inferior utilizando una Interfaz Cerebro-Máquina (BMI) de imaginación motora (MI). En estos pasos iniciales, se diseña un protocolo experimental para generar potenciales ErrP y NoEr...

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...

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 difcult to desig...

El Comité Español de Automática (CEA) es una asociación científica sin ánimo de lucro que impulsa el desarrollo, la investigación y las enseñanzasuniversitarias en Automática. Es miembro nacional de la Federación Internacional de Control Automático (IFAC), y celebra anualmente desde el año 1977 las Jornadas de Automática. Estas se organizan por dis...

El Comité Español de Automática (CEA) es una asociación científica sin ánimo de lucro que impulsa el desarrollo, la investigación y las enseñanzasuniversitarias en Automática. Es miembro nacional de la Federación Internacional de Control Automático (IFAC), y celebra anualmente desde el año 1977 las Jornadas de Automática. Estas se organizan por dis...

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 de...

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...

This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury. The BCI was evaluated in ten able-bodied subjects and two patients with spinal cord injuries. Five able-bodied subjects underwent a virtual reality (VR) training sess...

Introduction Brain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challen...

This chapter introduces the reader to the use of brain-machine interfaces (BMIs) for use in combination with robotic entities. The content explores topics ranging from the different applications of neurorobotics to the actual difficulties a neuroengineer must confront in order to implement and assess the performance of a BMI designed to work with a...

Las Jornadas de Automática (JA) son el evento más importante del Comité Español de Automática (CEA), entidad científico-técnica con más de cincuenta años de vida y destinada a la difusión e implantación de la Automática en la sociedad. Este año se celebra la cuadragésima tercera edición de las JA, que constituyen el punto de encuentro de la comunida...

Las Jornadas de Automática (JA) son el evento más importante del Comité Español de Automática (CEA), entidad científico-técnica con más de cincuenta años de vida y destinada a la difusión e implantación de la Automática en la sociedad. Este año se celebra la cuadragésima tercera edición de las JA, que constituyen el punto de encuentro de la comunida...

In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG a...

Spinal Cord Injury (SCI) refers to damage to the spinal cord that can affect different body functionalities. Recovery after SCI depends on multiple factors, being the rehabilitation therapy one of them. New approaches based on robot-assisted training offer the possibility to make training sessions longer and with a reproducible pattern of movements...

In this paper, the paradigm of the intention of speed changes from EEG signals with Riemannian classifiers methods is studied in 10 subjects. In addition, the best frequency band and how different electrode configurations affect the accuracy of the model are analyzed. In the prediction of the intention to change speed, results of 68.6% were obtaine...

Motor imagery (MI) brain-computer interfaces (BCI) have a critical function in the neurological rehabilitation of people with motor impairment. BCI are systems that employ brain activity to control any external device and MI is a commonly used control paradigm based on the imagination of a movement without executing it. The main limitation of these...

Neurorehabilitation has gradually become one of the most hopeful tools in some kind of injuries and diseases during the last decade. Several studies have shown that conscious movement effected by patients with mobility difficulties, assisted by a clinical device such as an exoskeleton, contributes positively to their mobility recovery, shortening t...

This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications. The aim of this work is to analyze the specific characteristics of lower-limb stimulation, identifying the strengths and weaknesses of these works and framing them with the current knowledge o...

The combination of a lower-limb exoskeleton with brain computer interfaces (BCI) can assist patients with motor impairment to walk again. In addition, it can promote the neural plasticity of the affected brain region. The present paper shows a research performed on seven able-bodied subjects that walked with an assistive exoskeleton controlled by e...

The design of solid interfaces based on the patterns of brain activity that underlie human decision-making are a field of interest in creating interfaces that allow recover the pathway between the brain and the muscular system to be rectified. In this work, a Brain Machine Interface is presented to detect the user's intention through the differenti...

Brain Machine Interfaces (BMI) combined with lower-limb exoskeletons can assist patients that have difficulties in walking. However, BMI need some calibration to adjust their parameters to each user. This process is time-consuming and can be fatiguing for the users. In this work, the optimal number of recordings needed to adjust a EEG-based BMI to...

Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is p...

Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimumfeatures and electrodes is pr...

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 percep...

Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this...

El uso de interfaces cerebro máquina (BCIs) supone un importante avance en el control de dispositivos para la rehabilitación de pacientes. Además, la detección de la atención durante la marcha puede ser fundamental a la hora de garantizar la seguridad en el control de estos dispositivos mediante electroencefalogramas (EEG) y evitar activaciones inv...

Las interfaces cerebro-máquina (BMIs de Brain- Machine Interfaces) son sistemas que utilizan la actividad cerebral para controlar dispositivos externos. Existen diversos paradigmas de control y uno de los más utilizados se basa en la imaginación motora (IM). La combinación de BMIs basadas en IM con dispositivos de asistencia como exoesqueletos robó...

The European University of Brain and Technology announces the NEURICOO event where Industry meets academics and students this 25th May. Please joint event via https://www.crowdcast.io/e/neuricoo

Lower-limb robotic exoskeletons are wearable devices that can be beneficial for people with lower-extremity motor impairment because they can be valuable in rehabilitation or assistance. These devices can be controlled mentally by means of brain–machine interfaces (BMI). The aim of the present study was the design of a BMI based on motor imagery (M...

Motor imagery (MI) is one of the most common paradigms used in brain-computer interfaces (BCIs). This mental process is defined as the imagination of movement without any motion. In some lower-limb exoskeletons controlled by BCIs, users have to perform MI continuously in order to move the exoskeleton. This makes it difficult to design a closed-loop...

Brain–Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason,...

This article shows the lessons learned from clinical trials of a new neurorehabilitation therapy for cerebro-vascular accident (CVA) patients. The new therapy is based on the combination of a transcranial direct current stimulation (tDCS) strategy, a brain–machine interface (BMI) based on electroencephalographic signals, and a pedaling system. The...

Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive i...

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the d...

Spinal cord injury (SCI) limits life expectancy and causes a restriction of patient's daily activities. In the last years, robotics exoskeletons have appeared as a promising rehabilitation and assistance tool for patients with motor limitations, as people that have suffered a SCI. The usability and clinical relevance of these robotics systems could...

This paper studies the direction changes during the gait by means of two different distributions of electrodes located in the motor, premotor and occipital areas. The objective is analyzing which areas are involved in the detection of the intention of turning while the person is walking. The signals in both options are characterized with frequency...

The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain–computer interface (BCI) based on elec...

The aim of this paper is to describe new methods for detecting the appearance of unexpected obstacles during normal gait from EEG signals, improving the accuracy and reducing the false positive rate obtained in previous studies. This way, an exoskeleton for rehabilitation or assistance of people with motor limitations commanded by a Brain-Machine I...

Hackathons are becoming these days more and more popular. The present chapter introduces the concept of brain on art hackathon. This new type of hackathon, based on the interaction of art, science, and engineering, is a successful event where learning, innovativeness, art and neuro-technology meet.

El análisis de las señales cerebrales para la asistencia en pacientes con movilidad reducida es un área de investigación continua en la que las nuevas tecnologías ofrecen un amplio espectro de posibilidades para la ayuda activa como los exoesqueletos y las interfaces cerebro-maquinas (BMI). En este trabajo nos centramos en realizar una interfaz BMI...

Contribution: A demonstration of a move from face-to-face to blended learning in an engineering Master's program by adding a parallel online group, which showed the synergy between blended and online approaches in terms of level of interaction, enrollment, student satisfaction, dropout rate, and final grades. Background: Education's transition from...

Lower-limb exoskeletons have been used in gait rehabilitation to facilitate the restoration of motor skills. These robotics systems could be complemented by Brain-Computer Interfaces (BCIs) to assist or rehabilitate people with walking disabilities. In this preliminary study, electroencephalography-based brain functional connectivity is analyzed du...

Transcranial direct current stimulation (tDCS) is a non-invasive technique for brain stimulation capable of modulating brain excitability. Although beneficial effects of tDCS have been shown, the underlying brain mechanisms have not been described. In the present study, we aim to investigate the effects of tDCS on EEG-based functional connectivity,...

The paper compares different signal processing algorithms and classifiers to evaluate the accuracy of a BMI based on lower-limb motor imagery. The methods were based on the analysis of the peaks of the different processing epochs for the alpha, beta and gamma EEG bands through the Marginal Hilbert Spectrum, Power Spectral Density and Fourier harmon...

The aim of this work was to test if a novel transcranial direct current stimulation (tDCS) montage boosts the accuracy of lower limb motor imagery (MI) detection by using a real-time brain-machine interface (BMI) based on electroencephalographic (EEG) signals. The tDCS montage designed was composed of two anodes and one cathode: one anode over the...

p>El uso de interfaces cerebro-máquina en personas que han sufrido un accidente cerebro-vascular puede ayudar en su proceso de rehabilitación mediante la implicación cognitiva del paciente. Dichas interfaces traducen las ondas cerebrales en comandos con el fin de controlar un dispositivo mecánico de movimiento asistido. No obstante, el control de e...

This work studies a novel transcranial direct current stimulation (tDCS) montage to improve a brain-machine interface (BMI) lower limb motor imagery detection. The tDCS montage is composed by two anodes and one cathode. One anode is located over the motor cortex and the other one over the cerebellum. Ten healthy subjects participated in this experi...

The use of motion assistance devices improves the rehabilitation process of patients that have motor disabilities. In the case these devices are controlled by brain-machine interfaces, the rehabilitation process can be improved due to neuroplasticity. However, in the case of lower limb rehabilitation, the limited accuracy of the control algorithms...

The purpose of this work is to strengthen the cortical excitability over the primary motor cortex (M1) and the cerebro-cerebellar pathway by means of a new transcranial direct current stimulation (tDCS) configuration to detect lower limb motor imagery (MI) in real time using two different cognitive neural states: relax and pedaling MI. The anode is...

The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is t...

The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and...

Transcranial direct stimulation (tDCS) is a technique for modulating brain excitability that has potential to be used in motor neurorehabilitation by enhancing motor activity, such as motor imagery (MI). tDCS effects depend on different factors, like current density and the position of the stimulating electrodes. This study presents preliminary res...

Brain-Computer Interfaces are one of the most interesting ways to work in rehabilitation and assistance programs to people who have problems in their lower limb to march. This paper presents evidence by means of statistical analysis sets that there are specific frequencies ranges on EEG signals while walking on four different surfaces: hard floor,...

This paper is intended to explain how the possibilities of enabling technologies (advanced metering infrastructures) can be expanded on to evaluate end uses at the demand-side level. For example, these data allow validating the effective response to market prices (energy markets) or system events (demand response), and besides, the possibilities th...

This work presents a mathematical tool applicable to the characterization and classification of power system events. Disturbances without a periodic pattern or with a nonlinear pattern require a more suitable tool than the Fourier series (Fast Fourier or Windowed Fourier Transforms). To overcome the difficulties, other tools have been broadly used...

The aim of this study is to propose a methodology in order to obtain a better support management decisions in terms of planning of bids and energy offers in real-time energy markets. Specifically, the authors use self-organising maps and statistical Ward's linkage to classify electricity market prices into different clusters (high homogeneity insid...

The objective of this research is to analyze the capacity of the Multilayer Perceptron Neural Network (MLP) versus Self-Organizing Map Neural Network (SOM) for Short-Term Load Forecasting. The MLP is one of the most commonly used networks. It can be used for classification problems, model construction, series forecasting and discrete control. For t...

Short-term forecasting is required by utility planners and electric system operators for tactical operational planning and day-to-day decision making. The forecasting is intended to obtain the system load demand over a period of hours or days, and it plays an important role in determining unit commitment, spinning reserve, economic power interchang...

Diesel-electric traction is a well known and established technology for railways operators, but this alternative has a considerable uncerainty for the future because electric traction has a considerable superiority. Besides, diesel-electric engines waste energy when resistivebraking is used. This non-regenerative braking decreases the overall effic...

The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario can not be achieved without the full participation of the electricity demand. The aim of this paper is to propose a procedure, through the detection of electricity-price patterns based...

The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario can not be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers an...

The objective of this paper is to show the capability of the Self-Organizing Maps (SOMs) to organize, to filter, to classify and to extract patterns from distributor, commercializer, aggregator or customer electrical demand databases -objective known as data mining-. This approach basically uses -to reach the above mentioned objectives-the historic...

Different methodologies are available for clustering and classification purposes. The objective of the research is to prove the capability of self-organising maps (SOMs) to classify customers and their response potential from distributor, commercialiser, or customer electrical demand databases, with the help of load response modelling methodologies...

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can ac...

This paper summarizes the research work performed to show the capability of a combination of tools based on Self-Organizing Maps (SOM) and Physically Based Load Models (PBLM) to classify and extract pat-terns from distributor, aggregator and customer electrical demand databases (the objective known as data mining). This approach basically uses low...

To manage efforts in energy efficiency, the Polytechnic University of Cartagena (UPCT) decided in 2003 to develop an ambitious project to reduce energy use intensity and costs during the period 2003–2008. To accomplish this objective in lighting end-use demand -one of the two main electrical uses together with space cooling/heating-, the UPCT joins...

This paper shows the capacity of modern computational techniques such as the self-organizing map (SOM) as a methodology to achieve the classification of the electrical customers in a commercial or geographical area. This approach allows to extract the pattern of customer behavior from historic load demand series. Several ways of data analysis from...