Ursula Costa

Upper limb portable motion analysis system based on inertial technology for neurorehabilitation purposes.

Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb’s orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.

Gait Training in Human Spinal Cord Injury Using Electromechanical Systems: Effect of Device Type and Patient Characteristics

OBJECTIVE To report the clinical improvements in spinal cord injury (SCI) patients associated with intensive gait training using electromechanical systems according to patient characteristics. DESIGN Prospective longitudinal study. SETTING Inpatient SCI rehabilitation center. PARTICIPANTS Adults with SCI (n=130). INTERVENTION Patients received locomotor training with 2 different electromechanical devices, 5 days per week for 8 weeks. MAIN OUTCOME MEASURES Lower-extremity motor score, Walking Index for Spinal Cord Injury, and 10-meter walking test data were collected at the baseline, midpoint, and end of the program. Patients were stratified according to the American Spinal Injury Association (ASIA) category, time since injury, and injury etiology. A subgroup of traumatic ASIA grade C and D patients were compared with data obtained from the European Multicenter Study about Human Spinal Cord Injury (EM-SCI). RESULTS One hundred and five patients completed the program. Significant gains in lower-limb motor function and gait were observed for both types of electromechanical device systems, to a similar degree. The greatest rate of improvement was shown in the motor incomplete SCI patients, and for patients <6 months postinjury. The positive response associated with training was not affected by injury etiology, age, sex, or lesion level. The trajectory of improvement was significantly enhanced relative to patients receiving the conventional standard of care without electromechanical systems (EM-SCI). CONCLUSIONS The use of electromechanical systems for intensive gait training in SCI is associated with a marked improvement in lower-limb motor function and gait across a diverse range of patients and is most evident in motor incomplete patients, and for patients who begin the regimen early in the recovery process.

Motor and gait improvement in patients with incomplete spinal cord injury induced by high-frequency repetitive transcranial magnetic stimulation.

OBJECTIVE To assess the effect of high-frequency repetitive transcranial magnetic stimulation (rTMS) on lower extremities motor score (LEMS) and gait in patients with motor incomplete spinal cord injury (SCI). METHOD The prospective longitudinal randomized, double-blind study assessed 17 SCI patients ASIA D. We assessed LEMS, modified Ashworth Scale (MAS), 10-m walking test (10MWT), Walking Index for SCI (WISCI II) scale, step length, cadence, and Timed Up and Go (TUG) test at baseline, after the last of 15 daily sessions of rTMS and 2 weeks later. Patients were randomized to active rTMS or sham stimulation. Three patients from the initial group of 10 randomized to sham stimulation entered the active rTMS group after a 3-week washout period. Therefore a total of 10 patients completed each study condition. Both groups were homogeneous for age, gender, time since injury, etiology, and ASIA scale. Active rTMS consisted of 15 days of daily sessions of 20 trains of 40 pulses at 20 Hz and an intensity of 90% of resting motor threshold. rTMS was applied with a double cone coil to the leg motor area. RESULTS There was a significant improvement in LEMS in the active group (28.4 at baseline and 33.2 after stimulation; P = .004) but not in the sham group (29.6 at baseline, and 30.9 after stimulation; P = .6). The active group also showed significant improvements in the MAS, 10MWT, cadence, step length, and TUG, and these improvements were maintained 2 weeks later. Following sham stimulation, significant improvement was found only for step length and TUG. No significant changes were observed in the WISCI II scale in either group. CONCLUSION High-frequency rTMS over the leg motor area can improve LEMS, spasticity, and gait in patients with motor incomplete SCI.

Accuracy of a P300 Speller for People with Motor Impairments: a Comparison

A Brain-Computer Interface (BCI) provides a completely new output pathway that can provide an additional option for a person to express himself/her self if he/she suffers a disorder like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury or other diseases which impair the function of the common output pathways which are responsible for the control of muscles. For a P300 based BCI a matrix of randomly flashing characters is presented to the participant. To spell a character the person has to attend to it and to count how many times the character flashes. Although most BCIs are designed to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on people suffering motor impairments, such as stroke or ALS, to measurements performed on healthy people. The overall accuracy of the persons with motor impairments reached 70.1% in comparison to 91% obtained for the group of healthy subjects. When looking at single subjects, one interesting example shows that under certain circumstances, when it is difficult for a patient to concentrate on one character for a longer period of time, the accuracy is higher when fewer flashes (i.e., stimuli) are presented. Furthermore, the influence of several tuning parameters is discussed as it shows that for some participants adaptations for achieving valuable spelling results are required. Finally, exclusion criteria for people who are not able to use the device are defined.

RETRACTED: Effects of High-Frequency Repetitive Transcranial Magnetic Stimulation on Motor and Gait Improvement in Incomplete Spinal Cord Injury Patients:

Kumru H, Benito J, Murillo N, et al. Effects of high-frequency repetitive transcranial magnetic stimulation on motor and gait improvement in incomplete spinal cord injury patients. Neurorehabil & Neural Repair. 2013;27:421-429. Original DOI: 10.1177/1545968312471901. The above article has been retracted because of substantial overlap with a previously published article in another journal.

A co-adaptive brain-computer interface for end users with severe motor impairment.

Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4) online, while the 22 end users alternately performed right hand movement imagery (MI), left hand MI and relax with eyes open (non-control state). After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01) better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01) better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.

An observational report of intensive robotic and manual gait training in sub-acute stroke

BackgroundThe use of automated electromechanical devices for gait training in neurological patients is increasing, yet the functional outcomes of well-defined training programs using these devices and the characteristics of patients that would most benefit are seldom reported in the literature. In an observational study of functional outcomes, we aimed to provide a benchmark for expected change in gait function in early stroke patients, from an intensive inpatient rehabilitation program including both robotic and manual gait training.MethodsWe followed 103 sub-acute stroke patients who met the clinical inclusion criteria for Body Weight Supported Robotic Gait Training (BWSRGT). Patients completed an intensive 8-week gait-training program comprising robotic gait training (weeks 0-4) followed by manual gait training (weeks 4-8). A change in clinical function was determined by the following assessments taken at 0, 4 and 8 weeks (baseline, mid-point and end-point respectively): Functional Ambulatory Categories (FAC), 10 m Walking Test (10 MWT), and Tinetti Gait and Balance Scales.ResultsOver half of the patients made a clinically meaningful improvement on the Tinetti Gait Scale (> 3 points) and Tinetti Balance Scale (> 5 points), while over 80% of the patients increased at least 1 point on the FAC scale (0-5) and improved walking speed by more than 0.2 m/s. Patients responded positively in gait function regardless of variables gender, age, aetiology (hemorrhagic/ischemic), and affected hemisphere. The most robust and significant change was observed for patients in the FAC categories two and three. The therapy was well tolerated and no patients withdrew for factors related to the type or intensity of training.ConclusionsEight-weeks of intensive rehabilitation including robotic and manual gait training was well tolerated by early stroke patients, and was associated with significant gains in function. Patients with mid-level gait dysfunction showed the most robust improvement following robotic training.

Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment.

Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks (“SMR-AdBCI”) have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (“Auto-AdBCI”) could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

Accuracy of a P300 speller for people with motor impairments

A Brain-Computer Interface (BCI) provides a completely new output pathway and so an additional possible way a person can express himself if he/she suffers disorders like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury or other diseases which impair the function of the common output pathways which are responsible for the control of muscles or impair the muscles. Although most BCIs are thought to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on 10 physically disabled people to the results of a previous study, taken of 100 healthy persons. We prove that, under certain constraints most patients are able to control a P300-based spelling device with almost the same accuracy than the healthy ones. Tuning parameters are discussed as well as criteria for people who are not able to use this device.

Online co-adaptive brain-computer interfacing: Preliminary results in individuals with spinal cord injury

Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for system setup and training of healthy users. However, there is little evidence as to whether co-adaptive ERD based BCI training paradigms could also benefit severely disabled users, including persons with spinal cord injury (SCI). Here, we present a preliminary study involving individuals with SCI at cervical level. In a cue-paced paradigm, our co-adaptive BCI analyzes the electroencephalogram from three bipolar derivations (C3, Cz, and C4), while the user alternately performs right hand movement imagery (MI), left hand MI and relax with eyes open. After less than five minutes of data collection, the BCI auto-calibrates and provides feedback for the MI task that can be classified better against relax with eyes open. The BCI then regularly recalibrates the underlying classifier model. In every calibration step, the system performs rigorous outlier rejection, selects the one out of six predefined logarithmic bandpower features (9 to 14 and 16 to 26Hz for the bipolars at C3, Cz and C4) that shows highest discriminability, and trains a linear discriminant analysis classifier. In under 30 min of training, all six tetraplegic users reached better than chance (p=0.01) online ERD based BCI control at an overall mean accuracy of 69.5 ± 6.4 %. These positive findings encourage us to evaluate the efficacy of adaptive BCI systems in users who have functional disability as a result of pathologies other than SCI.