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Few studies have investigated the psychometric properties of the Scale for Contraversive Pushing (SCP) in depth, and none have evaluated its ability to establish differential diagnosis between pusher behavior (PB) and thalamic astasia (TA).: To study the ability of the SCP to establish differential diagnosis, its reliability, content, construct, and internal validity in the assessment of subacute stroke patients.: 120 individuals were evaluated using the SCP over a four-week period of treatment. Intra- and inter-observer reliability, floor and ceiling effects, minimum detectable change (MDC), internal validity and sensitivity to change were explored. In addition, the Barthel Index and the Trunk Control Test were used to study their correlations with the SCP.: Discriminant validity provides evidence that the correlation between SCP items was large or moderate. Convergent validity demonstrated that the correlation of each item with the total score of the scale was high (at around 0.8). Sensitivity to change was large (W = 0.274). Intra- and inter-observer reliability were excellent (Intraclass Correlation Coefficient0.9; k0.8), except for items B standing and C sitting (k 0.7). The MDC was 1.39, and ceiling (8.333%) and floor (15.833%) effects were adequate. Cronbach's alpha (α) was equal to 0.901 (0.874-0.924) and McDonald's Omega (ω) was equal to 0.883 (0.856-0.973), showing excellent internal consistency.: The SCP is a reliable and valid tool which can successfully establish differential diagnosis between PB and TA and evaluate the changes generated by physiotherapy treatment.

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

Background: Stroke is the leading cause of disability in adults, producing a major personal and economic impact on those affected. The scientific evidence regarding the use of Motor Imagery (MI) as a preparatory process for motor control reinforces the need to explore this method as a complement to physical therapy.Objectives: The objectives of this systematic review were to determine the effectiveness of MI for functional recovery after stroke and to identify a possible intervention protocol, according to the level of existing scientific evidence.Methods: A comprehensive literature search was performed using Medline, Cochrane Library and PEDro databases. Studies were limited to those published between 2007 and 2017, and restricted to English and/or Spanish language publications.Results: Thirteen randomized clinical trials that met the inclusion criteria were included. The methodological quality of studies was determined using the Critical Review Form for Quantitative Studies, obtaining scores of 9-13 points out of 15. The level of evidence and strength of recommendations were assessed using the U.S. Preventive Services Task Force (USPSTF) assessment, obtaining levels IA and II-B1. Significant improvements were found in outcome measures evaluating upper limb functionality, balance and kinematic gait parameters.Conclusions: The use of MI combined with conventional rehabilitation is an effective method for the recovery of functionality after stroke. Due to the great heterogeneity in the scientific literature available, new lines of research are necessary, in order to include well-designed studies of good methodological quality and to establish a consensus regarding the most appropriate protocols.

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

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

[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).

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.

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.

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.

Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity. Minimizing the recalibration times is important to reduce the setup preparation and maximize the effective therapy time. To date, a systematic analysis of the effect of recalibration strategies in EEG-driven interfaces for motor rehabilitation has not yet been performed. Data from patients with stroke (4 patients, 8 sessions) and spinal cord injury (SCI) (4 patients, 5 sessions) undergoing two different paradigms (self-paced and cue-guided, respectively) are used to study the performance of the EEG-based classification of motor intentions. Four calibration schemes are compared, considering different combinations of training datasets from previous and/or the validated session. The results show significant differences in classifier performances in terms of the true and false positives (TPs) and (FPs). Combining training data from previous sessions with data from the validation session provides the best compromise between the amount of data needed for calibration and the classifier performance. With this scheme, the average true (false) positive rates obtained are 85.3% (17.3%) and 72.9% (30.3%) for the self-paced and the cue-guided protocols, respectively. These results suggest that the use of optimal recalibration schemes for EEG-based classifiers of motor intentions leads to enhanced performances of these technologies, while not requiring long calibration phases prior to starting the intervention.