Development of EEG-based technologies for the characterization and treatment of neurological diseases affecting the motor function

This thesis presents a set of studies applying signal processing and data mining techniques in real-time working systems to register, characterize and condition the movement-related cortical activity of healthy subjects and of patients with neurological disorders affecting the motor function. Patients with two of the most widespread neurological affections impairing the motor function are considered here: patients with essential tremor and patients who have suffered a cerebro-vascular accident. The different chapters in the presented thesis show results regarding the normal cortical activity associated with the planning and execution of motor actions with the upper-limb, and the pathological activity related to the patients' motor dysfunction (measurable with muscle electrodes or movement sensors). The initial chapters of the book present i) a revision of the basic concepts regarding the role of the cerebral cortex in the motor control and the way in which the electroencephalographic activity allows its analysis and conditioning, ii) a study on the cortico-muscular interaction at the tremor frequency in patients with essential tremor under the effects of a drug reducing their tremor, and finally iii) a study based on evolutionary algorithms that aims to identify cortical patterns related to the planning of a number of motor tasks performed with a single arm. In the second half of the thesis book, two brain-computer interface systems to be used in rehabilitation scenarios with essential tremor patients and with patients with a stroke are proposed. In the first system, the electroencephalographic activity is used to anticipate voluntary movement actions, and this information is integrated in a multimodal platform estimating and suppressing the pathological tremors. In the second case, a conditioning paradigm for stroke patients based on the identification of the motor intention with temporal precision is presented and tested with a cohort of four patients along a month during which the patients undergo eight intervention sessions. The presented thesis has yielded advances from both the technological and the scientific points of view in all studies proposed. The main contributions from the technological point of view are: ¿ The design of an integrated upper-limb platform working in real-time. The platform was designed to acquire information from different types of noninvasive sensors (EEG, EMG and gyroscopic sensors) characterizing the planning and execution of voluntary movements. The platform was also capable of processing online the acquired data and generating an electrical feedback. ¿ The development of signal processing and classifying techniques adapted to the kind of signal recorded in the two kinds of patients considered in this thesis (patients with essential tremor and patients with a stroke) and to the requirements of online processing and real-time single-trial function desired for BCI applications. Especially in this regard, an original methodology to detect onsets of voluntary movements using slow cortical potentials and cortical rhythms has been presented. ¿ The design and validation in real-time of asynchronous BCI systems using motor planning EEG segments to anticipate or detect when patients begin a voluntary movement with the upper-limb. ¿ The proof of concept of the advantages of an EEG system integrated in a multimodal human-robot interface architecture that constitutes the first multimodal interface using the combined acquisition of EEG, EMG and gyroscopic data, which allows the concurrent characterization of different parts of the body associated with the execution of a movement. The main scientific contributions of this thesis are: ¿ The study of the EEG-based anticipation of voluntary movements presented in Chapter 5 of the thesis was the first demonstration (to the author's knowledge) of the capacity of the EEG signal to provide reliable movement predictions based on single-trial classification of online data of healthy subjects and ET patients. This study also provides, for the first time, the results of a BCI system tested in ET patients and it represents an original approach to BCI applications for this group of patients. ¿ It has been presented the first neurophysiological study using EEG and EMG data to analyze the effects of a drug on cortical activity and tremors of patients with ET. In addition, the obtained results have shown for the first time that a significant correlation exists between the dynamics of specific cortical oscillations and pathological tremor manifestation as a consequence of the drug effects. ¿ It has been proposed for the first time an experiment to inspect whether the EEG signal carries enough information to classify up to seven different tasks performed with a single limb. Both the methodology applied and the validation procedure are also innovative in this sort of studies. ¿ It has been demonstrated for the first time the relevance of combining different cortical sources of information (such as BP and ERD) to estimate the initiation of voluntary movements with the upper-limb. In this line, special relevance may be given to the positive results achieved with stroke patients, improving the results presented by similar previous EEG-based studies by other research groups. It has also been proposed for the first time an upper-limb intervention protocol for stroke patients using BP and ERD patterns to provide proprioceptive feedback tightly associated with the patients' expectations of movement. The effects of the proposed intervention have been studied with a small group of patients

Event-related desynchronization-based versus bereitchaftspotential-based classfiers in stroke patiens

To compare the most well-known volitional movement-related electrophysiological phenomena for upper-limb movements in stroke patients, as source of movement classifiers.Peer Reviewe

Influence of common synaptic input to motor neurons on the neural drive to muscle in essential tremor

Tremor in essential tremor (ET) is generated by pathological oscillations at 4 to 12 Hz, likely originating at cerebello-thalamo-cortical pathways. However, the way in which tremor is represented in the output of the spinal cord circuitries is largely unknown because of the difficulties in identifying the behavior of individual motor units from tremulous muscles. By using novel methods for the decomposition of multichannel surface EMG, we provide a systematic analysis of the discharge properties of motor units in 9 ET patients, with concurrent recordings of EEG activity. This analysis allowed inferring the contribution of common synaptic inputs to motor neurons in ET. Motor unit short-term synchronization was significantly greater in ET patients than in healthy subjects. Further, the strong association between the degree of synchronization and the peak in coherence between motor unit spike trains at the tremor frequency indicated that the high synchronization levels were generated mainly by common synaptic inputs specifically at the tremor frequency. The coherence between EEG and motor unit spike trains demonstrated the presence of common cortical input to the motor neurons at the tremor frequency. Nonetheless, the strength of this input was uncorrelated to the net common synaptic input at the tremor frequency, suggesting a contribution of spinal afferents or secondary supraspinal pathways in projecting common input at the tremor frequency. These results provide the first systematic analysis of the neural drive to the muscle in ET and elucidate some of its characteristics that determine the pathological tremulous muscle activity.This work was funded by the EU Commission [grant number EU-FP7-2011-287739 (NeuroTREMOR)].Peer reviewe

Risk scores' performance and their impact on operative decision‑making in left‑sided endocarditis: a cohort study

Theaccuracy of contemporary risk scores in predicting perioperative mortality in infective endocarditis (IE) remains controversial. The aim is to evaluate the performance of existent mortality risk scores for cardiovascular surgery in IE and the impact on operability at high-risk thresholds. A single-center retrospective review of adult patients diagnosed with acute left-sided IE undergoing surgery from May 2014 to August 2019 (n = 142) was done. Individualized risk calculation was obtained according to the available mortality risk scores: EuroScore I and II, PALSUSE, Risk-E, Costa, De Feo-Cotrufo, AEPEI, STS-risk, STS-IE, APORTEI, and ICE-PCS scores. A cross-validation analysis was performed on the score with the best area under the curve (AUC). The 30-day survival was 96.5% (95%CI 91-98%). The score with worse area under the curve (AUC = 0.6) was the STS-IE score, while the higher was for the RISK-E score (AUC = 0.89). The AUC of the majority of risk scores suggested acceptable performance; however, statistically significant differences in expected versus observed mortalities were common. The cross-validation analysis showed that a large number of survivors (> 75%) would not have been operated if arbitrary high-risk threshold estimates had been used to deny surgery. The observed mortality in our cohort is significantly lower than is predicted by contemporary risk scores. Despite the reasonable numeric performance of the analyzed scores, their utility in judging the operability of a given patient remains questionable, as demonstrated in the cross-validation analysis. Future guidelines may advise that denial of surgery should only follow a highly experienced Endocarditis Team evaluation

Innocampus Explora: Nuevas formas de comunicar ciencia

[EN] Innocampus Explora aims to show the students of the Burjassot-Paterna campus of the Universitat de València how the different scientific degrees are interrelated. To do this we propose activities in which students and teachers work together to cover the interdisciplinary nature of science, both in everyday and professional issues. Throughout this course the activities developed relate to new ways to communicate science. With the development of this project we contribute to a transversal quality education for all the participating students.[ES] Innocampus Explora tiene por objetivo mostrar a los estudiantes del campus de Burjassot-Paterna de la Universitat de València cómo los diferentes grados científicos están interrelacionados. Para ello proponemos actividades en las que estudiantes y profesores trabajen conjuntamente para abarcar la interdisciplinariedad de la ciencia, tanto en temas cotidianos como profesionales. A lo largo de este curso las actividades desarrolladas se relacionan con las nuevas formas de comunicar ciencia. Con el desarrollo de este proyecto contribuimos a una formación transversal de calidad para todos los estudiantes participantes.Moros Gregorio, J.; Rodrigo Martínez, P.; Torres Piedras, C.; Montoya Martínez, L.; Peña Peña, J.; Pla Díaz, M.; Galarza Jiménez, P.... (2019). Innocampus Explora: Nuevas formas de comunicar ciencia. En IN-RED 2019. V Congreso de Innovación Educativa y Docencia en Red. Editorial Universitat Politècnica de València. 814-823. https://doi.org/10.4995/INRED2019.2019.10449OCS81482

Development of EEG-based technologies for the characterization and treatment of neurological diseases a ecting the motor function

Tesis Doctoral para la obtención del Título de Grado de Doctor. vii, 140 p. : il., diagr. Fecha de defensa de la Tesis Doctoral: 30 de octubre de 2014. Calificación: Sobresaliente cum laudemTREMOR European project. FP7-ICT-2007-224051. An ambulatory BCI-driven tremor suppression system based on functional electrical stimulationHYPER project. CSD2009-00067. Hybrid NeuroProsthetic and NeuroRobotic Devicesfor Functional Compensation and Rehabilitation of Motor Disor-dersThis thesis presents a set of studies applying signal processing and data mining techniques in real-time working systems to register, characterize and condition the movement-related cortical activity of healthy subjects and of patients with neurological disorders affecting the motor function. Patients with two of the most widespread neurological affections impairing the motor function are considered here: patients with essential tremor and patients who have suffered a cerebro-vascular accident. The different chapters in the presented thesis show results regarding the normal cortical activity associated with the planning and execution of motor actions with the upper-limb, and the pathological activity related to the patients' motor dysfunction (measurable with muscle electrodes or movement sensors). The initial chapters of the book present A) a revision of the basic concepts regarding the role of the cerebral cortex in the motor control and the way in which the electroencephalographic activity allows its analysis and conditioning, B) a study on the cortico-muscular interaction at the tremor frequency in patients with essential tremor under the effects of a drug reducing their tremor, and finally C) a study based on evolutionary algorithms that aims to identify cortical patterns related to the planning of a number of motor tasks performed with a single arm. In the second half of the thesis book, two brain-computer interface systems to be used in rehabilitation scenarios with essential tremor patients and with patients with a stroke are proposed. In the first system, the electroencephalographic activity is used to anticipate voluntary movement actions, and this information is integrated in a multimodal platform estimating and suppressing the pathological tremors. In the second case, a conditioning paradigm for stroke patients based on the identification of the motor intention with temporal precision is presented and tested with a cohort of four patients along a month during which the patients undergo eight intervention sessions.info:eu-repo/grantAgreement/EC/FP7/224051HYPER project. CSD2009-00067. Hybrid NeuroProsthetic and NeuroRobotic Devices for Functional Compensation and Rehabilitation of Motor DisordersPeer reviewe

Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study

[Background] The association between motor-related cortical activity and peripheral stimulation with temporal precision has been proposed as a possible intervention to facilitate cortico-muscular pathways and thereby improve motor rehabilitation after stroke. Previous studies with patients have provided evidence of the possibility to implement brain-machine interface platforms able to decode motor intentions and use this information to trigger afferent stimulation and movement assistance. This study tests the use a low-latency movement intention detector to drive functional electrical stimulation assisting upper-limb reaching movements of patients with stroke.Methods: An eight-sessions intervention on the paretic arm was tested on four chronic stroke patients along 1 month. Patients' intentions to initiate reaching movements were decoded from electroencephalographic signals and used to trigger functional electrical stimulation that in turn assisted patients to do the task. The analysis of the patients' ability to interact with the intervention platform, the assessment of changes in patients' clinical scales and of the system usability and the kinematic analysis of the reaching movements before and after the intervention period were carried to study the potential impact of the intervention.Results: On average 66.3 ± 15.7% of trials (resting intervals followed by self-initiated movements) were correctly classified with the decoder of motor intentions. The average detection latency (with respect to the movement onsets estimated with gyroscopes) was 112 ± 278 ms. The Fügl-Meyer index upper extremity increased 11.5 ± 5.5 points with the intervention. The stroke impact scale also increased. In line with changes in clinical scales, kinematics of reaching movements showed a trend toward lower compensatory mechanisms. Patients' assessment of the therapy reflected their acceptance of the proposed intervention protocol.Conclusions: According to results obtained here with a small sample of patients, Brain-Machine Interfaces providing low-latency support to upper-limb reaching movements in patients with stroke are a reliable and usable solution for motor rehabilitation interventions with potential functional benefits.[Methods] An eight-sessions intervention on the paretic arm was tested on four chronic stroke patients along 1 month. Patients' intentions to initiate reaching movements were decoded from electroencephalographic signals and used to trigger functional electrical stimulation that in turn assisted patients to do the task. The analysis of the patients' ability to interact with the intervention platform, the assessment of changes in patients' clinical scales and of the system usability and the kinematic analysis of the reaching movements before and after the intervention period were carried to study the potential impact of the intervention.[Results] On average 66.3 ± 15.7% of trials (resting intervals followed by self-initiated movements) were correctly classified with the decoder of motor intentions. The average detection latency (with respect to the movement onsets estimated with gyroscopes) was 112 ± 278 ms. The Fügl-Meyer index upper extremity increased 11.5 ± 5.5 points with the intervention. The stroke impact scale also increased. In line with changes in clinical scales, kinematics of reaching movements showed a trend toward lower compensatory mechanisms. Patients' assessment of the therapy reflected their acceptance of the proposed intervention protocol.[Conclusions] According to results obtained here with a small sample of patients, Brain-Machine Interfaces providing low-latency support to upper-limb reaching movements in patients with stroke are a reliable and usable solution for motor rehabilitation interventions with potential functional benefits.This research has been supported by Spanish Ministry of Science project HYPER-CSD2009-00067. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Peer reviewedPeer Reviewe

Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials

[Objective] Characterizing the intention to move by means of electroencephalographic activity can be used in rehabilitation protocols with patients’ cortical activity taking an active role during the intervention. In such applications, the reliability of the intention estimation is critical both in terms of specificity ‘number of misclassifications’ and temporal accuracy. Here, a detector of the onset of voluntary upper-limb reaching movements based on the cortical rhythms and the slow cortical potentials is proposed. The improvement in detections due to the combination of these two cortical patterns is also studied.[Approach] Upper-limb movements and cortical activity were recorded in healthy subjects and stroke patients performing self-paced reaching movements. A logistic regression combined the output of two classifiers: (i) a naïve Bayes classifier trained to detect the event-related desynchronization preceding the movement onset and (ii) a matched filter detecting the bereitschaftspotential. The proposed detector was compared with the detectors by using each one of these cortical patterns separately. In addition, differences between the patients and healthy subjects were analysed.[Main results] On average, 74.5 ± 13.8% and 82.2 ± 10.4% of the movements were detected with 1.32 ± 0.87 and 1.50 ± 1.09 false detections generated per minute in the healthy subjects and the patients, respectively. A significantly better performance was achieved by the combined detector (as compared to the detectors of the two cortical patterns separately) in terms of true detections (p = 0.099) and false positives (p = 0.0083).[Significance] A rationale is provided for combining information from cortical rhythms and slow cortical potentials to detect the onsets of voluntary upper-limb movements. It is demonstrated that the two cortical processes supply complementary information that can be summed up to boost the performance of the detector. Successful results have been also obtained with stroke patients, which supports the use of the proposed system in brain–computer interface applications with this group of patients.This work has been funded by grant from the Spanish Ministry of Science and Innovation CONSOLIDER INGENIO, project HYPER (Hybrid NeuroProsthetic and NeuroRobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders, CSD2009-00067), from Proyectos Cero of FGCSIC, Obra Social la Caixa, CSIC, and from the project PIE 201050E087.Peer reviewe

A multimodal human–robot interface to drive a neuroprosthesis for tremor management

Tremor is the most prevalent movement disorder, and its incidence is increasing with aging. In spite of the numerous therapeutic solutions available, 65% of those suffering from upper limb tremor report serious difficulties during their daily living. This gives rise to research on different treatment alternatives, amongst which wearable robots that apply selective mechanical loads constitute an appealing approach. In this context, the current work presents a multimodal human-robot interface to drive a neuroprosthesis for tremor management. Our approach relies on the precise characterization of the tremor to modulate a functional electrical stimulation system that compensates for it. The neuroprosthesis is triggered by the detection of the intention to move derived from the analysis of electroencephalographic activity, which provides a natural interface with the user. When a prediction is delivered, surface electromyography serves to detect the actual onset of the tremor in the presence of volitional activity. This information in turn triggers the stimulation, which relies on tremor parameters-amplitude and frequency-derived from a pair of inertial sensors that record the kinematics of the affected joint. Surface electromyography also yields a first characterization of the tremor, together with precise information on the preferred stimulation site. Apart from allowing for an optimized performance of the system, our multimodal approach permits the implementation of redundant methods to both enhance the reliability of the system and adapt to the specific needs of different users. Results with a representative group of patients illustrate the performance of the interface presented here and demonstrate its feasibility.Peer reviewe

Feedback Error Learning Controller for Functional Electrical Stimulation Assistance in a Hybrid Robotic System for Reaching Rehabilitation

© The Authors.Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.This work has been done with the financial support of the Ministry of Science and Innovation of Spain, project HYPER (CSD 2009-00067 Hybrid Neuroprosthetic and Neurorobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders)