Ander Ramos-Murguialday

Brain-machine interface in chronic stroke rehabilitation: a controlled study.

Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain–machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double‐blind sham‐controlled design proof of concept study.

Brain-Computer Interface in Stroke: A Review of Progress

Brain-computer interface (BCI) technology has been used for rehabilitation after stroke and there are a number of reports involving stroke patients in BCI-feedback training. Most publications have demonstrated the efficacy of BCI technology in post-stroke rehabilitation using output devices such as Functional Electrical Stimulation, robot, and orthosis. The aim of this review is to focus on the progress of BCI-based rehabilitation strategies and to underline future challenges. A brief history of clinical BCI-approaches is presented focusing on stroke motor rehabilitation. A context for three approaches of a BCI-based motor rehabilitation program is outlined: the substitutive strategy, classical conditioning and operant conditioning. Furthermore, we include an overview of a pilot study concerning a new neuro-forcefeedback strategy. This pilot study involved healthy participants. Finally we address some challenges for future BCI-based rehabilitation.

Brain-computer interfaces for communication and rehabilitation

Brain–computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals

Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works.

Neurophysiology of Robot-Mediated Training and Therapy: A Perspective for Future Use in Clinical Populations

The recovery of functional movements following injury to the central nervous system (CNS) is multifaceted and is accompanied by processes occurring in the injured and non-injured hemispheres of the brain or above/below a spinal cord lesion. The changes in the CNS are the consequence of functional and structural processes collectively termed neuroplasticity and these may occur spontaneously and/or be induced by movement practice. The neurophysiological mechanisms underlying such brain plasticity may take different forms in different types of injury, for example stroke vs. spinal cord injury (SCI). Recovery of movement can be enhanced by intensive, repetitive, variable, and rewarding motor practice. To this end, robots that enable or facilitate repetitive movements have been developed to assist recovery and rehabilitation. Here, we suggest that some elements of robot-mediated training such as assistance and perturbation may have the potential to enhance neuroplasticity. Together the elemental components for developing integrated robot-mediated training protocols may form part of a neurorehabilitation framework alongside those methods already employed by therapists. Robots could thus open up a wider choice of options for delivering movement rehabilitation grounded on the principles underpinning neuroplasticity in the human CNS.

Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients

OBJECTIVE 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.

Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation

Stroke is a cardiovascular accident within the brain resulting in motor and sensory impairment in most of the survivors. A stroke can produce complete paralysis of the limb although sensory abilities are normally preserved. Functional electrical stimulation (FES), robotics and brain computer interfaces (BCIs) have been used to induce motor rehabilitation. In this work we measured the brain activity of healthy volunteers using electroencephalography (EEG) during FES, passive movements, active movements, motor imagery of the hand and resting to compare afferent and efferent brain signals produced during these motor related activities and to define possible features for an online FES-BCI. In the conditions in which the hand was moved we limited the movement range in order to control the afferent flow. Although we observed that there is a subject dependent frequency and spatial distribution of efferent and afferent signals, common patterns between conditions and subjects were present mainly in the low beta frequency range. When averaging all the subjects together the most significant frequency bin comparing each condition versus rest was exactly the same for all conditions but motor imagery. These results suggest that to implement an on-line FES-BCI, afferent brain signals resulting from FES have to be filtered and time-frequency-spatial features need to be used.

Movement related slow cortical potentials in severely paralyzed chronic stroke patients

Movement-related slow cortical potentials (SCPs) are proposed as reliable and immediate indicators of cortical reorganization in motor learning. SCP amplitude and latency have been reported as markers for the brains computational effort, attention and movement planning. SCPs have been used as an EEG signature of motor control and as a main feature in Brain-Machine-Interfaces (BMIs). Some reports suggest SCPs are modified following stroke. In this study, we investigated movement-related SCPs in severe chronic stroke patients with no residual paretic hand movements preceding and during paretic (when they try to move) and healthy hand movements. The aim was to identify SCP signatures related to cortex integrity and complete paralysis due to stroke in the chronic stage. Twenty severely impaired (no residual finger extension) chronic stoke patients, of whom ten presented subcortical and ten cortical and subcortical lesions, underwent EEG and EMG recordings during a cue triggered hand movement (open/close) paradigm. SCP onset appeared and peaked significantly earlier during paretic hand movements than during healthy hand movements. Amplitudes were significantly larger over the midline (Cz, Fz) for paretic hand movements while contralateral (C4, F4) and midline (Cz, Fz) amplitudes were significantly larger than ipsilateral activity for healthy hand movements. Dividing the participants into subcortical only and mixed lesioned patient groups, no significant differences observed in SCP amplitude and latency between groups. This suggests lesions in the thalamocortical loop as the main factor in SCP changes after stroke. Furthermore, we demonstrated how, after long-term complete paralysis, post-stroke intention to move a paralyzed hand resulted in longer and larger SCPs originating in the frontal areas. These results suggest SCP are a valuable feature that should be incorporated in the design of new neurofeedback strategies for motor neurorehabilitation.

Cortex integrity relevance in muscle synergies in severe chronic stroke.

Background: Recent experimental evidence has indicated that the motor system coordinates muscle activations through a linear combination of muscle synergies that are specified at the spinal or brainstem networks level. After stroke upper limb impairment is characterized by abnormal patterns of muscle activations or synergies. Objective: This study aimed at characterizing the muscle synergies in severely affected chronic stroke patients. Furthermore, the influence of integrity of the sensorimotor cortex on synergy modularity and its relation with motor impairment was evaluated. Methods: Surface electromyography from 33 severely impaired chronic stroke patients was recorded during 6 bilateral movements. Muscle synergies were extracted and synergy patterns were correlated with motor impairment scales. Results: Muscle synergies extracted revealed different physiological patterns dependent on the preservation of the sensorimotor cortex. Patients without intact sensorimotor cortex showed a high preservation of muscle synergies. On the contrary, patients with intact sensorimotor cortex showed poorer muscle synergies preservation and an increase in new generated synergies. Furthermore, the preservation of muscle synergies correlated positively with hand functionality in patients with intact sensorimotor cortex and subcortical lesions only. Conclusion: Our results indicate that severely paralyzed chronic stroke patient with intact sensorimotor cortex might sculpt new synergy patterns as a response to maladaptive compensatory strategies.

Brain oscillatory signatures of motor tasks

Noninvasive brain-computer-interfaces (BCI) coupled with prosthetic devices were recently introduced in the rehabilitation of chronic stroke and other disorders of the motor system. These BCI systems and motor rehabilitation in general involve several motor tasks for training. This study investigates the neurophysiological bases of an EEG-oscillation-driven BCI combined with a neuroprosthetic device to define the specific oscillatory signature of the BCI task. Controlling movements of a hand robotic orthosis with motor imagery of the same movement generates sensorimotor rhythm oscillation changes and involves three elements of tasks also used in stroke motor rehabilitation: passive and active movement, motor imagery, and motor intention. We recorded EEG while nine healthy participants performed five different motor tasks consisting of closing and opening of the hand as follows: 1) motor imagery without any external feedback and without overt hand movement, 2) motor imagery that moves the orthosis proportional to the produced brain oscillation change with online proprioceptive and visual feedback of the hand moving through a neuroprosthetic device (BCI condition), 3) passive and 4) active movement of the hand with feedback (seeing and feeling the hand moving), and 5) rest. During the BCI condition, participants received contingent online feedback of the decrease of power of the sensorimotor rhythm, which induced orthosis movement and therefore proprioceptive and visual information from the moving hand. We analyzed brain activity during the five conditions using time-frequency domain bootstrap-based statistical comparisons and Morlet transforms. Activity during rest was used as a reference. Significant contralateral and ipsilateral event-related desynchronization of sensorimotor rhythm was present during all motor tasks, largest in contralateral-postcentral, medio-central, and ipsilateral-precentral areas identifying the ipsilateral precentral cortex as an integral part of motor regulation. Changes in task-specific frequency power compared with rest were similar between motor tasks, and only significant differences in the time course and some narrow specific frequency bands were observed between motor tasks. We identified EEG features representing active and passive proprioception (with and without muscle contraction) and active intention and passive involvement (with and without voluntary effort) differentiating brain oscillations during motor tasks that could substantially support the design of novel motor BCI-based rehabilitation therapies. The BCI task induced significantly different brain activity compared with the other motor tasks, indicating neural processes unique to the use of body actuators control in a BCI context.