Interaction with touchscreen smartphones in patients with essential tremor and healthy individuals.

INTRODUCTION: Smartphone use in biomedical research is becoming more prevalent in different clinical settings. We performed a pilot study to obtain information on smartphone use by patients with essential tremor (ET) and healthy controls, with a view to determining whether performance of touchscreen tasks is different between these groups and describing touchscreen interaction factors. METHOD: A total of 31 patients with ET and 40 sex- and age-matched healthy controls completed a descriptive questionnaire about the use of smartphones. Participants subsequently interacted with an under-development Android application, and performed 4 tests evaluating typical touchscreen interaction gestures; each test was performed 5 times. RESULT: The type of smartphone use and touchscreen interaction were not significantly different between patients and controls. Age and frequency of smartphone use are key factors in touchscreen interaction. CONCLUSION: Our results support the use of smartphone touchscreens for research into ET, although further studies are required.

Interaction with touchscreen smartphones in patients with essential tremor and healthy individuals. / Interacción con pantalla táctil de smartphone en pacientes con temblor esencial y sujetos sanos.

INTRODUCTION: Smartphones use in biomedical research is becoming more prevalent in different clinical settings. We performed a pilot study to obtain information on smartphone use by patients with essential tremor (ET) and healthy controls, with a view to determining whether performance of touchscreen tasks is different between these groups and describing touchscreen interaction factors. METHOD: A total of 31 patients with ET and 40 sex- and age-matched healthy controls completed a descriptive questionnaire about the use of smartphones. Participants subsequently interacted with an under-development Android application, and performed 4 tests evaluating typical touchscreen interaction gestures; each test was performed 5 times. RESULT: The type of smartphone use and touchscreen interaction were not significantly different between patients and controls. Age and frequency of smartphone use are key factors in touchscreen interaction. CONCLUSION: Our results support the use of smartphone touchscreens for research into ET, although further studies are required.

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.

[Training cortical signals by means of a BMI-EEG system, its evolution and intervention. A case report]. / Entrenamiento de las señales corticales a traves de un sistema BMI-EEG, evolucion e intervencion. A proposito de un caso.

INTRODUCTION: In the last years, new technologies such as the brain-machine interfaces (BMI) have been incorporated in the rehabilitation process of subjects with stroke. These systems are able to detect motion intention, analyzing the cortical signals using different techniques such as the electroencephalography (EEG). This information could guide different interfaces such as robotic devices, electrical stimulation or virtual reality. CASE REPORT: A 40 years-old man with stroke with two months from the injury participated in this study. We used a BMI based on EEG. The subject's motion intention was analyzed calculating the event-related desynchronization. The upper limb motor function was evaluated with the Fugl-Meyer Assessment and the participant's satisfaction was evaluated using the QUEST 2.0. The intervention using a physical therapist as an interface was carried out without difficulty. CONCLUSIONS: The BMI systems detect cortical changes in a subacute stroke subject. These changes are coherent with the evolution observed using the Fugl-Meyer Assessment.

TITLE: Entrenamiento de las señales corticales a traves de un sistema BMI-EEG, evolucion e intervencion. A proposito de un caso.Introduccion. En los ultimos años estan incorporandose nuevas tecnologias en el tratamiento fisioterapeutico de pacientes con ictus, como las interfaces cerebro-maquina ­brain-machine interface (BMI)­, capaces de detectar la intencion de movimiento analizando las señales corticales por medio de diferentes tecnicas, como la electroencefalografia (EEG). Estas señales se traducen en comandos con el fin de realizar una funcion. Caso clinico. Varon de 40 años con ictus de dos meses de evolucion, en el cual se empleo un dispositivo BMI-EEG. La intencion de movimiento del sujeto se analizo calculando la desincronizacion relacionada con el evento. La funcion motora del miembro superior fue evaluada con la escala de Fugl-Meyer, y el nivel de satisfaccion del paciente, mediante el cuestionario QUEST 2.0. La intervencion se llevo a cabo sin dificultad siendo el fisioterapeuta la interfaz. Conclusiones. Los sistemas BMI-EEG detectan cambios corticales en un sujeto con ictus subagudo. Estos cambios son coherentes con los cambios observados en escalas clinicas.

Electroencephalography as a post-stroke assessment method: An updated review. / Electroencefalografía como método de evaluación tras un ictus. Una revisión actualizada.

Given that stroke is currently a serious problem in the population, employing more reliable and objective techniques for determining diagnosis and prognosis is necessary in order to enable effective clinical decision-making. EEG is a simple, low-cost, non-invasive tool that can provide information about the changes occurring in the cerebral cortex during the recovery process after stroke. EEG provides data on the evolution of cortical activation patterns which can be used to establish a prognosis geared toward harnessing each patient's full potential. This strategy can be used to prevent compensation and maladaptive plasticity, redirect treatments, and develop new interventions that will let stroke patients reach their new maximum motor levels.

Locomotor training through a novel robotic platform for gait rehabilitation in pediatric population: short report.

BACKGROUND: Cerebral Palsy (CP) is a disorder of posture and movement due to a defect in the immature brain. The use of robotic devices as alternative treatment to improve the gait function in patients with CP has increased. Nevertheless, current gait trainers are focused on controlling complete joint trajectories, avoiding postural control and the adaptation of the therapy to a specific patient. This paper presents the applicability of a new robotic platform called CPWalker in children with spastic diplegia. FINDINGS: CPWalker consists of a smart walker with body weight and autonomous locomotion support and an exoskeleton for joint motion support. Likewise, CPWalker enables strategies to improve postural control during walking. The integrated robotic platform provides means for testing novel gait rehabilitation therapies in subjects with CP and similar motor disorders. Patient-tailored therapies were programmed in the device for its evaluation in three children with spastic diplegia for 5 weeks. After ten sessions of personalized training with CPWalker, the children improved the mean velocity (51.94 ± 41.97 %), cadence (29.19 ± 33.36 %) and step length (26.49 ± 19.58 %) in each leg. Post-3D gait assessments provided kinematic outcomes closer to normal values than Pre-3D assessments. CONCLUSIONS: The results show the potential of the novel robotic platform to serve as a rehabilitation tool. The autonomous locomotion and impedance control enhanced the children's participation during therapies. Moreover, participants' postural control was substantially improved, which indicates the usefulness of the approach based on promoting the patient's trunk control while the locomotion therapy is executed. Although results are promising, further studies with bigger sample size are required.

Predictive classification of self-paced upper-limb analytical movements with EEG.

The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.

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.

Upper-limb muscular electrical stimulation driven by EEG-based detections of the intentions to move: a proposed intervention for patients with stroke.

This study proposes an intervention for stroke patients in which electrical stimulation of muscles in the affected arm is supplied when movement intention is detected from the electroencephalographic signal. The detection relies on the combined analysis of two movement related cortical patterns: the event-related desynchronization and the bereitschaftspotential. Results with two healthy subjects and three chronic stroke patients show that reliable EEG-based estimations of the movement onsets can be generated (on average, 66.9 ± 26.4 % of the movements are detected with 0.42 ± 0.17 false activations per minute) which in turn give rise to electrical stimuli providing sensory feedback tightly associated to the movement planning (average detection latency of the onsets of the movements was 54.4 ± 287.9 ms).

A computational knowledge-based model for emulating human performance in the Iowa Gambling Task.

A new computational knowledge-based model for emulating human performance in decision making tasks is proposed. This model is mainly based on the knowledge acquired through past experience, the knowledge extracted from the environment and the relationships between the concepts that represent these two kinds of knowledge. The proposed model divides the decision making process into two phases. The first phase lies in the estimation of the decision outcomes using a net of concepts. In the second phase, the proposed model uses a value function to score each possible alternative. The design of the model focuses on some psychological and neurophysiological evidence from current research. In order to validate the model, it is compared with other widely used models that implement different theories of decision making under risk and uncertainty. The model comparison is centered on a well defined task, the Iowa Gambling Task, used in several psychological experiments. The comparison applies an evaluation method based on the optimization of each model in order to emulate human performance individually starting both the participant and the model from the same environmentally available information. The results show that the performance of the proposed model is quantitatively better than the other compared models. Besides, using relevant concepts extracted from interviews with the participants increases the performance of the proposed model.

Realistic retrospective dose assessments to members of the public around Spanish nuclear facilities.

In the frame of an epidemiological study carried out in the influence areas around the Spanish nuclear facilities (ISCIII-CSN, 2009. Epidemiological Study of The Possible Effect of Ionizing Radiations Deriving from The Operation of Spanish Nuclear Fuel Cycle Facilities on The Health of The Population Living in Their Vicinity. Final report December 2009. Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III, Consejo de Seguridad Nuclear. Madrid. Available from: http://www.csn.es/images/stories/actualidad_datos/especiales/epidemiologico/epidemiological_study.pdf), annual effective doses to public have been assessed by the Spanish Nuclear Safety Council (CSN) for over 45 years using a retrospective realistic-dose methodology. These values are compared with data from natural radiation exposure. For the affected population, natural radiation effective doses are in average 2300 times higher than effective doses due to the operation of nuclear installations (nuclear power stations and fuel cycle facilities). When considering the impact on the whole Spanish population, effective doses attributable to nuclear facilities represent in average 3.5×10(-5)mSv/y, in contrast to 1.6mSv/y from natural radiation or 1.3mSv/y from medical exposures.

An asynchronous BMI system for online single-trial detection of movement intention.

This paper presents an approach for an asynchronous BMI proposed as a switching part of a tremor suppression system developed for real-time continuous conditions. The main purpose of this BMI-switch is to anticipate the execution of self-initiated movements performed after relatively long periods of inactivity. The performance indicators used for the detector validation are specially suited for the continuous characteristic of the paradigm used and it is demonstrated that our ERD-based bayesian classifier solution is a reliable option, detecting a high rate of positive cases and generating very few false positives during long intervals of inactivity. The subjects analyzed for our detector validation were patients with neurological tremor caused by different pathologies in order to assure the adaptability of our system.