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Resumen Introduccion El uso de smartphones en investigacion biomedica esta creciendo rapidamente en diferentes entornos clinicos. Realizamos un estudio piloto para obtener informacion sobre el uso de smartphones en pacientes con temblor esencial (TE) y en sujetos sanos, con el objetivo de evaluar si la realizacion de diversas tareas con las pantallas tactiles difiere entre grupos y describir factores de esta interaccion. Metodo Se administro un cuestionario sobre el uso de smartphones a 31 pacientes con TE y 40 sujetos control apareados por edad y sexo. Acto seguido, los participantes interactuaron con una aplicacion Android en desarrollo y realizaron 4 test basados en diferentes modos de interaccion tipicos con pantallas tactiles, con 5 repeticiones de cada tarea. Resultado Los tipos de uso de smartphones asi como su interaccion no fueron significativamente diferentes entre pacientes y controles. La edad y el numero de usos del smartphone son factores clave en esta interaccion con pantallas tactiles. Conclusion Estas observaciones apoyan el uso de las pantallas tactiles de los smartphones para investigacion en TE, pero se requieren mas estudios.

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., This study was funded by the Spanish Ministry of Economy and Competitiveness (grant no. RTC-2015-3967-1, “NetMD-Plataforma para el seguimiento de Trastornos del Movimiento”).

Objective: Vojta therapy describes stereotypic widespread motor responses as a pattern of tonic muscle contractions during a peripherical pressure stimulation. The present work proposes to characterize the responses at muscles level to a specific tactile input based on Vojta therapy, assessed by sEMG, compared to a sham stimulation in healthy subjects. Methods: Surface electromyography (sEMG) signal was acquired with dipolar electrodes placed at wrist extensors of both forearms, right tibialis anterior, and top part of rectus abdominus, ground channel placed over the right olecranon. It was amplified and digitized by a 4-channel hub Biosignalsplux device (Plux Wireless Biosignals S.A., Lisboa, Portugal), sampled at 1000 Hz with 16-bit per channel. A continuous 10-minute record of the sEMG signal from the four electrodes were registered. Resting EEG during the first minute before the stimulation period was recorded by 64 active electrodes. Results: Statistically significant differences were showed between sham and experimental group. Experimental group participants were subjected to cluster analysis based on their muscle activation patterns, generating three different models of activation. Differences in the previous resting cortical activity in left superior frontal area were found between clusters that activated limb muscles and the cluster that did not. Conclusions: Vojta specific stimulation area activates innate muscle responses assessed by sEMG in healthy subjects, compared to a sham stimulation. Significance: This characterization might be helpful to the prescription and application of Vojta therapy in an individual-basis for non-neurophysiologically damaged adult subjects.

Severe traumatic brain injury residual cognitive impairments significantly impact the quality of life. EEG-based neurofeedback is a technique successfully used in traumatic brain injury and stroke to rehabilitate cognitive and motor sequelae. There are not individualized comparisons of the effects of EEG-based neurofeedback versus conventional neuropsychological rehabilitation. We present a case study of a traumatic brain injury subject in whom eight sessions of a neuropsychological rehabilitation protocol targeting attention, executive functions, and working memory as compared with a personalized EEG-based neurofeedback protocol focused on the electrodes and bands that differed from healthy subjects (F3, F1, Fz, FC3, FC1, and FCz), targeting the inhibition of theta frequency band (3 Hz−7 Hz) in the same number of sessions. Quantitative EEG and neuropsychological testing were performed. Clear benefits of EEG-based neurofeedback were found in divided and sustained attention and several aspects related to visuospatial skills and the processing speed of motor-dependent tasks. Correlative quantitative EEG changes justify the results. EEG-based neurofeedback is probably an excellent complementary technique to be considered to enhance conventional neuropsychological rehabilitation. post-print 1462 KB

Background and Objectives: The motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico-spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere. Mental representation techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind, randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain–Computer Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI), dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention, after each intervention and 15 days after the last session. Conclusions: This trial will show the additive value of VR immersive motor imagery as an adjuvant therapy combined with a known effective neuromodulation approach opening new perspectives for clinical rehabilitation protocols.

Background: Brain’s is stimulated by Vojta Therapy through selected body areas activating stored innate motor programs which are exported as coordinate movement and muscle contractions to trunk and limbs. The aim of this pilot study is to know the responses at cortical level to a specific tactile input, assessed by electroencephalography (EEG), compared to a sham stimulation, in healthy subjects. Methods: A randomized-controlled trial was conducted. Participants were randomly distributed into two groups: a non-specific tactile input-group (non-STI-group) (n = 20) and a Vojta specific tactile input-group (V-STI-group) (n = 20). The non-STI-group was stimulated in a non specific area (quadriceps distal area) and V-STI-group was stimulated in a specific area (intercostal space, at the mammillary line between the 7th and 8th ribs) according to the Vojta therapy. Recording was performed with EEG for 10 min considering a first minute of rest, 8 min during the stimulus and 1 min after the stimulus. EEG activity was recorded from 32 positions with active Ag/AgCl scalp electrodes following the 10–20 system. The continuous EEG signal was split into consecutive segments of one minute. Results: The V-STI-group showed statistically significant differences in the theta, low alpha and high alpha bands, bilaterally in the supplementary motor (SMA) and premotor (PMA) areas (BA6 and BA8), superior parietal cortex (BA5, BA7) and the posterior cingulate cortex (BA23, BA31). For the V-STI-group, all frequency bands presented an initial bilateral activation of the superior and medial SMA (BA6) during the first minute. This activation was maintained until the fourth minute. During the fourth minute, the activation decreased in the three frequency bands. From the fifth minute, the activation in the superior and medial SMA rose again in the three frequency bands Conclusions: Our findings highlight that the specific stimulation area at intercostal space, on the mammillary line between 7 and 8th ribs according to Vojta therapy differentially increased bilateral activation in SMA (BA6) and Pre-SMA (BA8), BA5, BA7, BA23 and BA31 in the theta, low and high alpha bands in healthy subjects. These results could indicate the activation of innate locomotor circuits during stimulation of the pectoral area according to the Vojta therapy., The authors would like to thank the volunteers for participating in this study.

Background: Brain connectivity has shown to be a key characteristic in the study of both Parkinson’s Disease (PD) and the response of the patients to the dopaminergic medication. Time series analysis has been used here for the first time to study brain connectivity changes during motor activation in PD. Methods: A 64-channel EEG signal was registered during unilateral motor activation and resting-state in 6 non-demented PD patients before and after the administration of levodopa and in 6 matched healthy controls. Spectral entropy correlation, coherence, and interhemispheric divergence differences among PD patients and controls were analyzed under the assumption of stationarity of the time series. Results: During the motor activation test, PD patients showed an increased correlation coefficient (both hands p < 0.001) and a remarkable increase in coherence in all frequency range compared to the generalized reduction observed in controls (both hands p < 0.001). The Kullback­–Leibler Divergence (KLD) of the Spectral Entropy between brain hemispheres was observed to increase in controls (right hand p = 0.01; left hand p = 0.015) and to decrease in PD patients (right hand p = 0.02; left hand p = 0.002) with motor activation. Conclusions: Our results suggest that the oscillatory activity of the different cortex areas within healthy brains is relatively independent of the rest. PD brains exhibit a stronger connectivity which grows during motor activation. The levodopa mitigates this anomalous performance., This research was supported mainly by FEDER funds. Dr. Romero is supported by the Ministry of Economy and Competitiveness (grant DPI2015-68664-C4-3-R (MINECO/FEDER), NeuroMOD Therapy development and evaluation of motor and cognitive impact for Parkinson´s disease rehabilitation). M.D. del Castillo, J.I. Serrano are supported by the Ministry of Economy and Competitiveness (grant DPI2015-68664-C4-1-R (MINECO/FEDER), NeuroMOD.

Background Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. Objectives The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. Type This was a systematic review. Literature Survey Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. Methodology This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. Synthesis After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. Conclusions This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. Level of Evidence II

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. Resumen: Actualmente, el ictus representa un problema grave en la población, por lo que es necesario emplear técnicas de evaluación de diagnóstico y pronóstico más fiables y objetivas que permitan una eficaz toma de decisiones clínicas. La electroencefalografía es una herramienta sencilla, de bajo coste y no invasiva, que puede proporcionar conocimiento acerca de los cambios que acontecen en el córtex cerebral en el proceso de recuperación tras un ictus. Proporciona datos de evolución de los patrones de activación corticales, que permiten establecer un pronóstico para aprovechar el máximo potencial de las personas evitando compensaciones y plasticidad maladaptativa, redireccionar los tratamientos y desarrollar nuevas intervenciones con el objetivo de alcanzar el nuevo máximo nivel motor de los pacientes que han sufrido un ictus. Keywords: Stroke, Electroencephalography, Event-related desynchronisation, Event-related synchronisation, Movement-related cortical potentials, Plasticity, Palabras clave: Ictus, Electroencefalografía, Desincronización relacionada con eventos, Sincronización relacionada con eventos, Potenciales corticales relacionados con el movimiento, Plasticidad

Resumen: Actualmente, el ictus representa un problema grave en la población, por lo que es necesario emplear técnicas de evaluación de diagnóstico y pronóstico más fiables y objetivas que permitan una eficaz toma de decisiones clínicas. La electroencefalografía es una herramienta sencilla, de bajo coste y no invasiva, que puede proporcionar conocimiento acerca de los cambios que acontecen en el córtex cerebral en el proceso de recuperación tras un ictus. Proporciona datos de evolución de los patrones de activación corticales, que permiten establecer un pronóstico para aprovechar el máximo potencial de las personas evitando compensaciones y plasticidad maladaptativa, redireccionar los tratamientos y desarrollar nuevas intervenciones con el objetivo de alcanzar el nuevo máximo nivel motor de los pacientes que han sufrido un ictus. Abstract: 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. Palabras clave: Ictus, Electroencefalografía, Desincronización relacionada con eventos, Sincronización relacionada con eventos, Potenciales corticales relacionados con el movimiento, Plasticidad, Keywords: Stroke, Electroencephalography, Event-related desynchronisation, Event-related synchronisation, Movement-related cortical potentials, Plasticity

Pathological tremor is a common but also highly complex movement disorder, affecting about 5% of population over the age of 65. Different methodologies have been proposed for its quantification and analysis. Nevertheless, the discrimination between Parkinsonian and Essential tremor remains a looming challenge in clinical practice, greatly impacting both patient treatment and the development of new interventions. In this study, the discriminative powers of cortical thickness features, extracted from magnetic resonance imaging of 21 ET, 15 PD patients and 18 healthy control subjects have been presented and discussed. A total of 129 volumetric features (whole brain, except cerebellum) and 152 cortical thickness features (average plus standard deviation of the thickness of the different cortical areas) have been extracted from each subject. Data mining of these characteristics has been employed to identify the most discriminative structural features for tremor diagnosis. These features were then combined in advanced classification models that yielded accuracy of up to 100% in discrimination of examined ET and PD patients, with the volume and thickness of enthorinal cortex as the most discriminative feature. Noteworthy, when compared to healthy age-matched controls, the identified MRI-based features indicate the structural changes of the brain and cortex in both PD and ET group of patients, potentially illuminating the neurodegenerative nature of both pathologies studied. This is an important finding by itself as the neurodegenerative aspects of ET have not yet been rigorously proven.

Background The Neurological Disorders in Central Spain, second survey (NEDICES-2) is a population-based, closed-cohort study that will include over 8000 subjects aged ≥55 years. It will also include a biobank. Objective The objective of this study was to evaluate all major aspects of the NEDICES-2 (methods, database, screening instruments, and questionnaires, as well as interexpert rating of the neurological diagnoses) in each one of the planned areas (all of them in central Spain) and to test the possibility of obtaining biological samples from each participant. Methods A selection of patients and participants of the planned NEDICES-2 underwent face-to-face interviews including a comprehensive questionnaire on demographics, current medications, medical conditions, and lifestyle habits. Biological samples (blood, saliva, urine, and hair) were also obtained. Furthermore, every participant was examined by a neurologist. Results In this pilot study, 567 study participants were enrolled (196 from hospitals and 371 from primary care physician lists). Of these 567, 310 completed all study procedures (questionnaires and the neurological evaluation). The study was time-consuming for several primary care physicians. Hence, a few primary care physicians from some areas refused to participate, which led to a reconfiguration of study areas. In addition, the central biobank needed to be supplemented by the biobanks of local Spanish National Health System hospitals. Conclusions Population-based epidemiological surveys, such as the NEDICES-2, require a pilot study to evaluate the feasibility of all aspects of a future field study (population selection, methods and instruments to be used, neurological diagnosis agreement, and data collection)., Our thanks to all collaborators in this feasibility pilot study, the Instituto de Salud Carlos III and the Biomedical Research Networking Center on Neurodegenerative Diseases (CIBERNED), and the two hospital computer experts (Victoria Garcia Baquero and Teresa Garcia Morales), who created the website (http://nedices2.ciberned.com/estudio-de-salud-nedices-2/). We would also like to thank Miss Jennifer Gates for the editing and proofreading of the manuscript.

Motor imagery (MI) has been suggested to provide additional benefits when included in more traditional approaches of physical therapy for children with cerebral palsy (CP). Regardless, little is understood about the underlying neurological substrates that might justify its supposed benefits. In this work, we studied resting-state (RS) electroencephalography (EEG) recordings of five children with CP that underwent a MI virtual-reality (VR) intervention. Our aim was to explore functional connectivity (FC) patterns alterations following this intervention through the formalism of graph theory, performing both group and subject-specific analyses. We found that FC patterns were more consistent across subjects prior to the MI-VR intervention, shifting along the anterior-posterior axis, post-intervention, for the β and γ bands. Additionally, group FC patterns were not found for the α range. Furthermore, intra-subject analyses reinforced the existence of large inter-subject variability and the need for a careful exploration of individual pattern alterations. Such patterns also hinted at a dependency between short-term functional plasticity mechanisms and the EEG frequency bands. Although our sample size is small, we provide a longitudinal analysis framework that can be replicated in future studies, especially at the group level, and whose foundation can be easily extended to verify the validity of our hypotheses.

BACKGROUND The Neurological Disorders in Central Spain, second survey (NEDICES-2) is a population-based, closed-cohort study that will include over 8000 subjects aged ≥55 years. It will also include a biobank. OBJECTIVE The objective of this study was to evaluate all major aspects of the NEDICES-2 (methods, database, screening instruments, and questionnaires, as well as interexpert rating of the neurological diagnoses) in each one of the planned areas (all of them in central Spain) and to test the possibility of obtaining biological samples from each participant. METHODS A selection of patients and participants of the planned NEDICES-2 underwent face-to-face interviews including a comprehensive questionnaire on demographics, current medications, medical conditions, and lifestyle habits. Biological samples (blood, saliva, urine, and hair) were also obtained. Furthermore, every participant was examined by a neurologist. RESULTS In this pilot study, 567 study participants were enrolled (196 from hospitals and 371 from primary care physician lists). Of these 567, 310 completed all study procedures (questionnaires and the neurological evaluation). The study was time-consuming for several primary care physicians. Hence, a few primary care physicians from some areas refused to participate, which led to a reconfiguration of study areas. In addition, the central biobank needed to be supplemented by the biobanks of local Spanish National Health System hospitals. CONCLUSIONS Population-based epidemiological surveys, such as the NEDICES-2, require a pilot study to evaluate the feasibility of all aspects of a future field study (population selection, methods and instruments to be used, neurological diagnosis agreement, and data collection).

Objective: Although the cerebello-thalamo-cortical network has often been suggested to be of importance in the pathogenesis of essential tremor (ET), the origins of tremorgenic activity in this disease are not fully understood. We used a combination of cortical thickness imaging and neurophysiological studies to analyze whether the severity of tremor was associated with anatomical changes in the brain in ET patients. Methods: Magnetic resonance imaging (MRI) and a neurophysiological assessment were performed in 13 nondemented ET patients. High field structural brain MRI images acquired in a 3T scanner and analyses of cortical thickness and surface were carried out. Cortical reconstruction and volumetric segmentation was performed with the FreeSurfer image analysis software. We used high-density surface electromyography (hdEMG) and inertial measurement units (IMUs) to quantify the tremor severity in upper extrimities of patients. In particular, advanced computer tool was used to reliably identify discharge patterns of individual motor units from surface hdEMG and quantify motor unit synchronization. Results: We found significant association between increased motor unit synchronization (i.e., more severe tremor) and cortical changes (i.e., atrophy) in widespread cerebral cortical areas, including the left medial orbitofrontal cortex, left isthmus of the cingulate gyrus, right paracentral lobule, right lingual gyrus, as well as reduced left supramarginal gyrus (inferior parietal cortex), right isthmus of the cingulate gyrus, left thalamus, and left amygdala volumes. Interpretation: Given that most of these brain areas are involved in controlling movement sequencing, ET tremor could be the result of an involuntary activation of a program of motor behavior used in the genesis of voluntary repetitive movements., This work was funded by FEDER grant ; National Institutes of Health grant ; NINDS grants R01 NS39422;, R01 NS094607;, R01 NS085136;, R01 NS073872;, and R01 NS088257; European Commission grant ICT‐2011‐287739; Ministry of Economy and Competitiveness grant RTC‐2015‐3967‐1; Spanish Health Research Agency grants FIS PI12/01602; and FIS PI16/00451; Spanish Ministry of Economy and Competitiveness grants DPI‐2015‐68664‐C4‐1‐R; and DPI2015‐72638‐EXP; Claire O'Neil Essential Tremor Research grant ; Slovenian Research Agency grant J2‐7357.

La extracción quirúrgica de los terceros molares supone la síntesis de los tejidos incididos previamente. El método de síntesis más utilizado en la práctica diaria del Cirujano Oral es la sutura con materiales convencionales guiados por una aguja para favorecer la cicatrización por primera intención, sin embargo, en la actualidad se ha puesto en boga la utilización de materiales adhesivos a base de Cianoacrilato para reemplazar las suturas convencionales, con el objetivo de disminuir el dolor postoperatorio, la dehiscencia de las heridas, el riesgo de infecciones, entre otros factores no menos importantes. Objetivo: El objetivo de este estudio predictivo es analizar comparativamente el dolor y la cicatrización de la herida postquirúrgica de terceros molares con la utilización de materiales de sutura adhesiva frente a suturas convencionales de vicryl 4/0 en cirugía de terceros molares retenidos. Materiales y métodos: Para este estudio predictivo se midieron los resultados arrojados de dos técnicas de síntesis en tejidos mucosos a través de la utilización de materiales de sutura convencional (vicryl 4/0 y sutura adhesiva a base de cianoacrilato. Se seleccionaron 30 de un universo de 137 pacientes que acuden al quirófano de la FOUCE y a clínicas particulares del cantón Sangolquí - Pichincha, en los que se realizaron 60 incisiones lineales de 3 cm de longitud, de ambos sexos, de entre 16 y 23 años de edad sin antecedentes patológicos personales. 30 incisiones correspondientes al tercer molar inferior izquierdo fueron sometidas a síntesis con material de sutura convencional y 30 incisiones correspondientes al tercer molar inferior derecho se sellaron con sutura adhesiva a base de cianoacrilato. Para valorar el dolor postquirúrgico se utilizó la técnica de escala visual análoga (EVA) y para evaluar la dehiscencia de los tejidos se utilizó el método de inspección y observación. Los dos métodos se utilizaron al segundo, quinto y octavo día postquirúrgico. Resultados: Los resultados arrojados por el estudio demuestran una gran efectividad en la disminución del dolor postquirúrgico y en la reducción de la dehiscencia de los colgajos al utilizar técnicas adhesivas de sutura a base de cianoacrilatos (Glubran2).

[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).

The term Cerebral Palsy (CP) is a set of neurological disorders that appear in infancy or early childhood and permanently affect body movement and muscle coordination. The prevalence of CP is two–three per 1000 births. Emerging rehabilitation therapies through new strategies are needed to diminish the assistance required for these patients, promoting their functional capability. This paper presents a new robotic platform called CPWalker for gait rehabilitation in patients with CP, which allows them to start experiencing autonomous locomotion through novel robot-based therapies. The platform (smart walker + exoskeleton) is controlled by a multimodal interface that gives high versatility. The therapeutic approach, as well as the details of the interactions may be defined through this interface. CPWalker concept aims to promote the earlier incorporation of patients with CP to the rehabilitation treatment and increases the level of intensity and frequency of the exercises. This will enable the maintenance of therapeutic methods on a daily basis, with the intention of leading to significant improvements in the treatment outcomes., El término Parálisis Cerebral (PC) es un conjunto de trastornos neurológicos que aparecen en la infancia o en la niñez temprana y que afectan permanentemente al movimiento del cuerpo y a la coordinación muscular. La prevalencia de la PC es de dos-tres por cada 1000 nacimientos. Se necesitan nuevas terapias de rehabilitación a través de nuevas estrategias para disminuir la asistencia requerida por estos pacientes, promoviendo su capacidad funcional. Este trabajo presenta una nueva plataforma robótica llamada CPWalker para la rehabilitación de la marcha en pacientes con parálisis cerebral, que les permite comenzar a experimentar la locomoción autónoma a través de novedosas terapias basadas en robots. La plataforma (andador inteligente + exoesqueleto) está controlada por una interfaz multimodal que proporciona una gran versatilidad. El enfoque terapéutico, así como los detalles de las interacciones, pueden definirse a través de esta interfaz. El concepto CPWalker pretende promover la incorporación más temprana de los pacientes con parálisis cerebral al tratamiento de rehabilitación y aumenta el nivel de intensidad y frecuencia de los ejercicios. Esto permitirá el mantenimiento de los métodos terapéuticos a diario, con la intención de que se produzcan mejoras significativas en los resultados del tratamiento.

Patients with tremor can benefit from wearable robots managing their tremor during daily living. To achieve this, the interfaces controlling such robotic systems must be able to estimate the user's intention to move and to distinguish it from the undesired tremor. In this context, analysis of electroencephalographic activity is of special interest, since it provides information on the planning and execution of voluntary movements. This paper proposes an adaptive and asynchronous EEG-based system for online detection of the intention to move in patients with tremor. An experimental protocol with separated self-paced wrist extensions was used to test the ability of the system to detect the intervals preceding voluntary movements. Six healthy subjects and four essential tremor patients took part in the experiments. The system predicted 60 ± 10% of the movements with the control subjects and 42 ± 27% of the movements with the patients. The ratio of false detections was low in both cases (1.5 ± 0.1 and 1.4 ± 0.5 false activations per minute with the controls and patients, respectively). The prediction period with which the movements were detected was higher than in previous similar studies (1.06 ± 1.02 s for the controls and 1.01 ± 0.99 s with the patients). Additionally, an adaptive and fixed design were compared, and it was the adaptive design that had a higher number of movement detections. The system is expected to lead to further development of more natural interfaces between the assistive devices and the patients wearing them.

Several robotic platforms were recently developed aimed at improving the locomotion capacity of people with gait impairment. Most of these gait trainers are limited to treadmill training, which is not a motivating condition for children with cerebral palsy (CP). This paper presents a pilot study done with two children with spastic CP, who trained with a new robotic platform called CPWalker during five weeks. This experimental device is a novel over ground prototype for gait rehabilitation with body weight support for children with CP. After rehabilitation training, both patients improved the mean velocity, cadence and step length. Moreover, the comparison between pre and post-kinematics analysis without the robot shows specific developments for each subject depending on the focus of the therapy (mainly trunk or hip flexion-extension).

Cerebral Palsy (CP) is a disorder of posture and movement due to an imperfection or lesion in the immature brain. CP is often associated to sensory deficits, cognition impairments, communication and motor disabilities, behaviour issues, seizure disorder, pain and secondary musculoskeletal problems. New strategies are needed to help to promote, maintain, and rehabilitate the functional capacity, and thereby diminish the dedication and assistance required and the economical demands that this condition represents for the patient, the caregivers and the whole society. This paper describes the conceptualization and development of the integrated CPWalker robotic platform to support novel therapies for CP rehabilitation. This platform (Smart Walker + exoskeleton) is controlled by a multimodal interface to establish the interaction of CP children with robot-based therapies. The objective of these therapies is to improve the physical skills of children with CP and similar disorders. CPWalker concept will promote the earlier incorporation of CP patients to the rehabilitation therapy and increase the level of intensity and frequency of the exercises according to the task, which will enable the maintenance of therapeutic methods in daily basis, with the intention to lead to significant improvements in the treatment outcome.

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.

SMS language presents special phenomena and important deviations from natural language. Every day, an impressive amount of chat messages, SMS messages, and e-mails are sent all over the world. This widespread use makes important the development of systems that normalize SMS language into natural language. However, typical machine translation approaches are difficult to adapt to SMS language because of many irregularities that are shown by this kind of language. This paper presents a new approach for SMS normalization that combines lexical and phonological translation techniques with disambiguation algorithms at two different levels: lexical and semantic. The method proposed does not depend on big annotated corpus, which is difficult to build and is applied in two different domains showing its easiness of adaptation across different languages and domains. The results obtained by the system outperform some of the existing methods of SMS normalization despite the fact that the Spanish language and the corpus created have some features that complicate the normalization task.

Sentence and short-text semantic similarity measures are becoming an important part of many natural language processing tasks, such as text summarization and conversational agents. This paper presents SyMSS, a new method for computing short-text and sentence semantic similarity. The method is based on the notion that the meaning of a sentence is made up of not only the meanings of its individual words, but also the structural way the words are combined. Thus, SyMSS captures and combines syntactic and semantic information to compute the semantic similarity of two sentences. Semantic information is obtained from a lexical database. Syntactic information is obtained through a deep parsing process that finds the phrases in each sentence. With this information, the proposed method measures the semantic similarity between concepts that play the same syntactic role. Psychological plausibility is added to the method by using previous findings about how humans weight different syntactic roles when computing semantic similarity. The results show that SyMSS outperforms state-of-the-art methods in terms of rank correlation with human intuition, thus proving the importance of syntactic information in sentence semantic similarity computation.

[ES] Existen diversas evidencias que indican que los déficits motores en los pacientes de parálisis cerebral se asocian con problemas en la planificación motora que, a su vez, apuntan a una mermada capacidad para imaginar movimientos. La imaginación motora se ha revelado como una herramienta efectiva en el aprendizaje y la adquisición de habilidades motoras ya que comparte estructuras neuronales similares con la ejecución motora. En este trabajo se presenta un paradigma basado en un juego de realidad virtual para guiar la actividad mental del paciente que sirve a dos fines: estudiar su capacidad de imaginar movimientos e implicar y motivar al paciente en el entrenamiento de dicha capacidad. El estudio ha involucrado cuatro niños con parálisis cerebral espástica (edad media = 13.25 años, DS = 1.5) con lesión cerebral bilateral. Los resultados obtenidos del análisis de su actividad electroencefalográfica muestran que estos pacientes son capaces de emplear la imaginación motora en una tarea de marcha, indicada por la presencia del fenómeno ERD (Event Related Desynchronization) en zonas corticales motoras, independientemente de su nivel funcional y de los miembros afectados., [EN] There are several evidences showing that motor disorders in patients with cerebral palsy are associated with problems in motor planning, which, in turn, denote a diminished capability to imagine movements. Motor imagery appears like an effective means in learning and acquiring motor skills since it shares similar neural structures to those ones used in motor execution. In this paper, a paradigm based on a virtual reality game that drives the patient’s electroencephalographic signal is presented. This study aims, on the one hand, to analyze the patients’ ability of imagining movements and, on the other hand, to involve and motivate them in order to implement this ability. The research work has engaged four children with spastic cerebral palsy (mean age = 13.25, SD = 1.5) with bilateral brain damage. After analyzing their electroencephalographic signal, the results show that these patients are able of using motor imagery in a walking task, as indicated by the presence of ERD (event related desynchronization) in cortical motor areas, regardless their functional impairment and affected body extremities., Este trabajo ha sido financiado parcialmente por los proyectos CP-WALKER (DPI2012-39133-C03-01), MD (PIE201650E055) y NeuroMOD (DPI2015-68664-C4-1-R)

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., 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.

Stroke motor rehabilitation strategies using neuromodulation paradigms that take advantage of the motor predictive characteristics of the electroencephalographic signal are currently subject to extensive research. Such rehabilitation strategies follow a top-down approach, in which targeted neurophysiological changes in the central nervous system are expected to induce functional improvement. This chapter presents a series of studies regarding processing algorithms to detect motor intentionality and a neuromodulation paradigm to improve the upper-limb functionality. The experiments were developed and tested with stroke patients.

The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents belonging to an imbalanced sample. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents, the system combines the predictions of the learners by applying evolutionary techniques as well. The system relies on a modular, flexible architecture that makes no assumptions about the design of learners or the number of learners available and guarantees the independence of the thematic domain.

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.

Note: As originally published there ares errors in this document. The author names were presented as "J. Ibanez, J.I. Serrano, M.D. del Castillo, E. Monge, F. Molina, F.M. Rivas, I. Alguacil, J.C. Miangolarra and J.L. Pons" but were instead intended to be: "E. Monge-Pereira, F. Molina-Rueda, F. Rivas-Montero, I. Alguacil-Diego, and J.C. Miangolarra-Page" as noted here. The article PDF remains as originally published., 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 % (std: 26.4) of the movements are detected with 0.42 (std: 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 (std: 287.9 ms)).

[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.

Stroke patients may present motor impairments that in many cases require an intensive rehabilitation process with experts helping the patient to recover the functionality of the affected limb. A target during this rehabilitation process is to induce neural plasticity in brain regions associated with the motor control of the affected limb. Electrical stimulation tightly synchronized with the intention to perform a movement has proven to be an effectiveway of enhancing cortical excitability in healthy subjects. The electroencephalogram can help to detect voluntary movements online.We propose here an Electroencephalographybased system aimed to detect the instants atwhich stroke patients attempt to start voluntary movements with the affected upperlimb. To accomplish this, the analysis of the cortical rhythms and their variations are used. In the preliminary results obtained with 3 chronic stroke patients, 63±14% of the movements were detected with a temporal precision in the detections of the onsets of the movements of -126±313 ms.

Stroke is a leading cause of permanent motor disability. At present, novel rehabilitation techniques focus on ways of promoting brain plasticity leading to functional rehabilitation of the patients. Among these techniques, the integration of Brain-Computer Interfaces in the rehabilitation process represents an encouraging area of active research. The measure of the brain signals during the execution of movements yields a relevant view of the motor-related amount of activity in specific areas of the brain. We expect that the combination of these brain signals with other sensors will lead to more natural interfaces between the patients and the rehabilitation devices, and to new means of tracking the patients’ involvement in the rehabilitation process. We present here preliminar results and future studies using Electroencephalography signals in a rehabilitation system for stroke survivors.

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.

The language used in electronic communications such as emails, chats and SMS texts presents special phenomena and important deviations from natural language. Typical machine translation approaches are difficult to adapt to SMS language due to the many irregularities this kind of language shows. This paper presents a new approach for SMS normalization that combines lexical and phonological translation techniques with disambiguation algorithms at two different levels: lexical and semantic. The results obtained by the system outperform some of the existing methods of SMS normalization despite the fact that the corpus created has some features that complicates the normalization task.

[EN] BCIs provide a channel for sending commands to the external environment by using electrophysiological measurements recording brain activity. The algorithm transforming the input signal into the output commands and the method used to extract the signal features for the algorithm are the key components for an optimum behaviour of the BCI. This paper presents a machine learning- based method to select these features and a classifier algorithm for building a predictive model from the EEg signal of patients with tremor. This model analyses and classifies the signal in a continous, asynchronous and adaptive way finding to identify the patient movement intention in order for a control system to cancel the tremor during the movement execution., [ES] Las Interfaces Cerebro-Computador proporcionan un canal para enviar órdenes al mundo exterior haciendo uso de medidas electrofisiológicas de la actividad cerebral. En este artículo se presenta la combinación de un método de selección de características y un algoritmo de clasificación probabilístico para construir el modelo predictivo de la intención anticipada de movimiento voluntario de pacientes con temblor a partir de un solo ensayo. Los resultados obtenidos muestran una potencial de discriminación del 70%, una tasa de error aceptable (6.6%) y una rápida respuesta (cada 250 ms), lo que indica que esta combinación es una buena base para la construcción de ICCs que no requieran entrenamiento del usuario de forma personalizada, asíncrona y adaptativa., Este trabajo ha sido realizado con la financiación de la Comisión de la Unión Europea dentro del 7 Programa Marco, para el proyecto con referencia ICT-2007-224051: “TREMOR: An ambulatory BCI-driven tremor suppression system based on functional electrical stimulation” y por el Ministerio de Ciencia e Innovación, con el proyecto IMAGENeuroMAPS (TEC2006-13966-C03-03) “Integración de resonancia magnética y electroencefalografía. Aplicación al fundamento y uso de ICCs por discapacitados”.

Owing to the growing amount of digital information stored in natural language, systems that automatically process text are of crucial importance and extremely useful. There is currently a considerable amount of research work (Sebastiani, 2002; Crammer et al., 2003) using a large variety of machine learning algorithms and other Knowledge Discovery in Databases (KDD) methods that are applied to Text Categorization (automatically labeling of texts according to category), Information Retrieval (retrieval of texts similar to a given cue), Information Extraction (identification of pieces of text that contains certain meanings), and Question/Answering (automatic answering of user questions about a certain topic). The texts or documents used can be stored either in ad hoc databases or in the World Wide Web. Data mining in texts, the well-known Text Mining, is a case of KDD with some particular issues: on one hand, the features are obtained from the words contained in texts or are the words themselves. Therefore, text mining systems faces with a huge amount of attributes. On the other hand, the features are highly correlated to form meanings, so it is necessary to take the relationships among words into account, what implies the consideration of syntax and semantics as human beings do. KDD techniques require input texts to be represented as a set of attributes in order to deal with them. The text-to-representation process is called text or document indexing, and the attributes and called indexes. Accordingly, indexing is a crucial process in text mining because indexed representations must collect, only with a set of indexes, most of the information expressed in natural language in the texts with the minimum loss of semantics, in order to perform as well as possible.

Beer is a low-alcohol beverage brewed from natural ingredients and may contain appreciable amounts of bioactive compounds, mainly antioxidants, which are associated with health effects. Polyphenols and melanoidins are the major natural antioxidants in beer. Polyphenol and melanoidin contents are largely influenced by genetic and agricultural factors in the raw material and by technological factors in the brewing. Common beer polyphenols include flavonols, phenolic acids, catechins, procyanidins, tannins and chalcones. Beer also contains Maillard reaction products, largely formed during the malting and brewing process. Various assays have shown that beer has greater antioxidant capacity than white wine, but less than red wine. Nevertheless, in several countries the consumption of beer contributes significantly to the total intake of polyphenols and to the antioxidant capacity of the diet. Beer in Spain (150 ml/person/day) provides 7.2% of the daily dietary intake of polyphenols and accounts for around 3% of the total antioxidant capacity of the diet. Further research is needed to elucidate the potential role of beer in the prevention of chronic diseases associated with the intake of antioxidants.

A situation consisting in evaluating and choosing among alternative actions can be managed from the point of view of Decision Making (DM). This paper presents an approach to design and develop Decision Support Systems (DSS) to be applied in emergency situations. In these situations the decision maker is under heavy stress because each different decision implies different important outcomes related with human and economic losses. First of all, a domain knowledge base has to be built from both the properties of emergency situations and the actions devoted to counteract them. From this knowledge, three different DM methods, based on the Probability Theory and the Possibility Theory, process the incoming emergency information and choose the best action for putting out the emergency situation. The resulting decisions of each method over a set of plausible emergency situations can be evaluated by a domain expert and the method with the best average performance can be built in the DSS. This DSS can help a decision maker find out an optimal decision in a short period of time maximizing security and minimizing stress.