Browsing by Author "Ramos-Murguialday, A."
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Item Brain-machine interfaces for rehabilitation in stroke: A review: A review(2018-07) López-Larraz, E.; Sarasola-Sanz, A.; Irastorza-Landa, N.; Birbaumer, N.; Ramos-Murguialday, A.; Medical TechnologiesBACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.Item Brain–computer interfaces in the completely locked-in state and chronic stroke(Elsevier B.V., 2016) Chaudhary, U.; Birbaumer, N.; Ramos-Murguialday, A.; Medical TechnologiesBrain–computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery.Item Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients(2014-12-01) Spüler, M.; Walter, A.; Ramos-Murguialday, A.; Naros, G.; Birbaumer, N.; Gharabaghi, A.; Rosenstiel, W.; Bogdan, M.; Medical TechnologiesObjective. Recently, there have been several approaches to utilize a brain-computer interface (BCI) for rehabilitation with stroke patients or as an assistive device for the paralyzed. In this study we investigated whether up to seven different hand movement intentions can be decoded from epidural electrocorticography (ECoG) in chronic stroke patients. Approach. In a screening session we recorded epidural ECoG data over the ipsilesional motor cortex from four chronic stroke patients who had no residual hand movement. Data was analyzed offline using a support vector machine (SVM) to decode different movement intentions. Main results. We showed that up to seven hand movement intentions can be decoded with an average accuracy of 61% (chance level 15.6%). When reducing the number of classes, average accuracies up to 88% can be achieved for decoding three different movement intentions. Significance. The findings suggest that ipsilesional epidural ECoG can be used as a viable control signal for BCI-driven neuroprosthesis. Although patients showed no sign of residual hand movement, brain activity at the ipsilesional motor cortex still shows enough intention-related activity to decode different movement intentions with sufficient accuracy.Item An EEG-Based Brain-Machine Interface to Control a 7-Degrees of Freedom Exoskeleton for Stroke Rehabilitation(Springer International Publishing, 2017) Sarasola-Sanz, A.; López-Larraz, E.; Irastorza-Landa, N.; Klein, J.; Valencia, D.; Belloso, A.; Morin, F. O.; Spüler, M.; Birbaumer, N.; Ramos-Murguialday, A.; Tecnalia Research & Innovation; Medical TechnologiesBrain machine interfaces (BMIs) have previously been utilized to control rehabilitation robots with promising results. The design and development of more dexterous and user-friendly rehabilitation platforms is the next challenge to be tackled. We built a novel platform that uses an electro-encephalograpy-based BMI to control a multi-degree of freedom exoskeleton in a rehabilitation framework. Its applicability to a clinical scenario is validated here with six healthy subjects and a chronic stroke patient using motor imagery and movements attempts. Therefore, this study presents a potential system to carry out fully-featured motor rehabilitation therapies.Item EMG Discrete Classification Towards a Myoelectric Control of a Robotic Exoskeleton in Motor Rehabilitation(Springer International Publishing, 2017) Irastorza-Landa, N.; Sarasola-Sanz, A.; Shiman, F.; López-Larraz, E.; Klein, J.; Valencia, D.; Belloso, A.; Morin, F. O.; Birbaumer, N.; Ramos-Murguialday, A.; Tecnalia Research & Innovation; Medical TechnologiesMyoelectric control constitutes a promising interface for robot-aided motor rehabilitation therapies. The development of accurate classifiers and suitable training protocols for this purpose are still challenging. In this study, eight healthy participants underwent electromyography (EMG) recordings while they performed reaching movements in four directions and five different hand movements wearing an exoskeleton on their right upper-limb. We developed an offline classifier based on a back-propagation artificial neural network (ANN) trained with the waveform length as time-domain feature extracted from EMG signals to classify discrete movements. A maximum overall classification performance of 75.54% 5.17 and 67.37%. We demonstrated that similar or better classification results could be achieved using a small number of electrodes placed over the main muscles involved in the movement instead of a large set of electrodes. This work is a first step towards a discrete decoding-based myoelectric control for a motor rehabilitation exoskeleton.Item Facilitation of completely paralyzed forearm muscle activity in chronic stroke patients(2013) Garcia-Cossio, E.; Birbaumer, N.; Ramos-Murguialday, A.; Medical TechnologiesStroke is the main cause of hemiparesis in developed countries. Very often upper limbs are compromised and the hemiparesis is characterized by abnormal muscle activations especially at the level of the wrist and fingers (distal muscles). In this study we investigated the stability and strength of paretic upper limb muscle activity during different bilateral movements eliciting different postures and muscle recruitment. We recorded surface EMG of 45 severe chronic stroke patients without residual finger extension during six bimanual movements. Sixteen bipolar-EMG electrodes were placed on both upper limbs at the level of proximal and distal areas. We extracted the waveform length from the EMG data, in order to investigate muscle activity level at the paralyzed muscles during all movements. Our results indicated that different positions during multi-joint movements of the upper limb facilitated the contraction of the affected muscles (forearm extensors) involuntarily during the movement in which this activation was not expected (e.g. abduction of the upper arm) in more than 64% of the patients. Here, we show that severe affected chronic stroke patients can induce a higher activation of the paretic muscles of the forearm by changing the upper limb posture. This might be an important hint to design multi-joint coordinated movements involving proximal and distal musculature for stroke motor rehabilitation.Item First Steps Towards Understanding How Non-Invasive Magnetic Stimulation Affects Neural Firing at Spinal Cord(IEEE Computer Society, 2019-05-16) Ortego-Isasa, I.; Martins, A.; Birbaumer, N.; Ramos-Murguialday, A.; Medical TechnologiesMagnetic stimulation using commercial transcranial magnetic stimulators (TMS) and coils is becoming an established tool for neurostimulation. However, when applied at the lumbar region it is not clear which neural structures are stimulated and especially, if the spinal cord (SC) can be stimulated. Computational modeling with realistic human body models is a promising tool to understand better the basic mechanisms of the stimulation. In this study we have used a realistic model to calculate the current density (J) distribution and magnitude under different output power levels of a commercial stimulator to describe the electromagnetic effects on the different tissues. Our results suggest that spinal cord stimulation with TMS is possible. However, significant muscle contraction is produced due to the high stimulation needed, which might make this stimulation non-practical. The spatial resolution of this technology is very poor to stimulate specific parts of the SC only. Although the stimulation aims at SC structures, we observed that most of the current does not reach the SC, but the cerebrospinal fluid (CSF). All together, these results represent a first step towards understanding and optimizing magnetic transpinal stimulation.Item Movement related cortical potentials change after EEG-BMI rehabilitation in chronic stroke(2013) Yilmaz, O.; Oladazimi, M.; Cho, W.; Brasil, F.; Curado, M.; Cossio, E. Garcia; Braun, C.; Birbaumer, N.; Ramos-Murguialday, A.; Medical TechnologiesMovement related cortical potentials (MRCPs) have been studied for many years and proposed as reliable and immediate indicators of cortical reorganizations in motor learning and after stroke. It has been reported that decrease in amplitude and later onset of MRCPs reflect less mental effort and shorter planning time during a motor task. In this study MRCPs preceding hand movements in severe chronic stroke were investigated in an EEG screening paradigm (patients performed hand open and close for paretic and healthy hand) before and after a one-month online-EEG-Brain-Machine-Interface neurorehabilitation intervention coupled with physiotherapy. Five severely impaired (no residual finger extension) chronic stoke patients were enrolled in the study. We observed that MRCPs peak amplitude over Cz during paretic hand movement attempts decreased significantly after compared to before intervention. Furthermore, MRCP onset was significantly later over central regions during paretic hand movements after compared to before intervention. There were no significant pre-post differences during healthy hand movements. Our results suggest that our patients needed less mental effort and shorter planning time after intervention. We demonstrated for the first time significant MRCP changes after a neurorehabilitation intervention (BMI + physiotherapy) in severe chronic stroke patients.Item Neuromuscular electrical stimulation induced brain patterns to decode motor imagery(2013-09) Vidaurre, C.; Pascual, J.; Ramos-Murguialday, A.; Lorenz, R.; Blankertz, B.; Birbaumer, N.; Müller, K. R.; Medical TechnologiesObjective: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. Methods: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. Results: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. Conclusion: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. Significance: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).