Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback.

BACKGROUND: Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes. METHODS: This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients' performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients. 12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training. RESULTS: Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients. CONCLUSIONS: Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.

Boosting the traditional physiotherapist approach for stroke spasticity using a sensorized ankle foot orthosis: a pilot study.

BACKGROUND: Spasticity is a motor disorder that is commonly treated manually by a physical therapist (PhT) stretching the muscles. Recent data on learning have demonstrated the importance of human-to-human interaction in improving rehabilitation: cooperative motor behavior engages specific areas of the motor system compared with execution of a task alone. OBJECTIVES: We hypothesize that PhT-guided therapy that involves active collaboration with the patient (Pt) through shared biomechanical visual biofeedback (vBFB) positively impacts learning and performance by the Pt during ankle spasticity treatment. A sensorized ankle foot orthosis (AFO) was developed to provide online quantitative data of joint range of motion (ROM), angular velocity, and electromyographic activity to the PhT and Pt during the treatment of ankle spasticity. METHODS: Randomized controlled clinical trial. Ten subacute stroke inpatients, randomized into experimental (EXP) and control (CTRL) groups, underwent six weeks of daily treatment. The EXP group was treated with an active AFO, and the CTRL group was given an inactive AFO. Spasticity, ankle ROM, ankle active and passive joint speed, and coactivation index (CI) were assessed at enrollment and after 15-30 sessions. RESULTS: Spasticity and CI (p < 0.005) decreased significantly after training only in the EXP group, in association with a significant rise in active joint speed and active ROM (p < 0.05). Improvements in spasticity (p < 0.05), active joint speed (p < 0.001), and CI (p < 0.001) after treatment differed between the EXP and CTRL groups. CONCLUSIONS: PhT-Pt sharing of exercise information, provided by joint sensorization and vBFB, improved the efficacy of the conventional approach for treating ankle spasticity in subacute stroke Pts.

Brain-computer interface boosts motor imagery practice during stroke recovery.

OBJECTIVE: Motor imagery (MI) is assumed to enhance poststroke motor recovery, yet its benefits are debatable. Brain-computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI. The efficacy of BCI-monitored MI practice as add-on intervention to usual rehabilitation care was evaluated in a randomized controlled pilot study in subacute stroke patients. METHODS: Twenty-eight hospitalized subacute stroke patients with severe motor deficits were randomized into 2 intervention groups: 1-month BCI-supported MI training (BCI group, n = 14) and 1-month MI training without BCI support (control group; n = 14). Functional and neurophysiological assessments were performed before and after the interventions, including evaluation of the upper limbs by Fugl-Meyer Assessment (FMA; primary outcome measure) and analysis of oscillatory activity and connectivity at rest, based on high-density electroencephalographic (EEG) recordings. RESULTS: Better functional outcome was observed in the BCI group, including a significantly higher probability of achieving a clinically relevant increase in the FMA score (p < 0.03). Post-BCI training changes in EEG sensorimotor power spectra (ie, stronger desynchronization in the alpha and beta bands) occurred with greater involvement of the ipsilesional hemisphere in response to MI of the paralyzed trained hand. Also, FMA improvements (effectiveness of FMA) correlated with the changes (ie, post-training increase) at rest in ipsilesional intrahemispheric connectivity in the same bands (p < 0.05). INTERPRETATION: The introduction of BCI technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke patients with severe motor impairments.

P300 latency Jitter occurrence in patients with disorders of consciousness: Toward a better design for Brain Computer Interface applications.

In this study the P300 latency jitter has been explored in an EEG data set collected from a group of patients with disorders of consciousness (DOC; n=13) that was administered with an auditory Oddball paradigm under passive and active conditions. A method based on wavelet transform was applied to estimate single trial P300 waveforms. Preliminary results showed that 5 Vegetative State (VS) and 8 Minimally Conscious Staten (MCS) patients exhibited significantly higher values of P300 latency jitter as compared to those obtained from a control group of 12 healthy subjects. In addition, the magnitude of the P300 latency jitter negatively correlated with patients' clinical status. The existence of such phenomenon might substantially limit an effective use of Brain Computer Interface systems for communication.

High-resolution EEG techniques for brain-computer interface applications.

High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.

Developing wearable bio-feedback systems: a general-purpose platform.

Microprocessors, even those in PocketPCs, have adequate power for many real-time biofeedback applications for disabled people. This power allows design of portable or wearable devices that are smaller and lighter, and that have longer battery life compared to notebook-based systems. In this paper, we discuss a general-purpose hardware/software solution based on industrial or consumer devices and a C++ framework. Its flexibility and modularity make it adaptable to a wide range of situations. Moreover, its design minimizes system requirements and programming effort, thus allowing efficient systems to be built quickly and easily. Our design has been used to build two brain computer interface systems that were easily ported from the Win32 platform.

The use of EEG modifications due to motor imagery for brain-computer interfaces.

The opening of a communication channel between brain and computer [brain-computer interface (BCI)] is possible by using changes in electroencephalogram (EEG) power spectra related to the imagination of movements. In this paper, we present results obtained by recording EEG during an upper limb motor imagery task in a total of 18 subjects by using low-resolution surface Laplacian, different linear and quadratic classifiers, as well as a variable number of scalp electrodes, from 2 to 26. The results (variable correct classification rate of mental imagery between 75% and 95%) suggest that it is possible to recognize quite reliably ongoing mental movement imagery for BCI applications.