Milica Djuric-Jovicic

Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface

Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here we evaluated the effect and the underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesize that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were divided into two training groups. Movement-related cortical potentials (MRCPs) were detected by electroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInonassociative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency, diffusion tensor imaging (DTI) data, and the excitability of the corticospinal tract to the target muscle [tibialis anterior (TA)] were quantified. The TA motor evoked potential (MEP) increased significantly after the BCIassociative intervention, but not for the BCInonassociative group. FM scores (0.8 ± 0.46 point difference, P = 0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. Corticospinal tract integrity on DTI did not correlate with clinical or physiological changes. For the BCI as applied here, the precise coupling between the brain command and the afferent signal was imperative for the behavioral, clinical, and neurophysiological changes reported. This association may become the driving principle for the design of BCI rehabilitation in the future. Indeed, no available BCIs can match this degree of functional improvement with such a short intervention.

Kinematics of Gait: New Method for Angle Estimation Based on Accelerometers

A new method for estimation of angles of leg segments and joints, which uses accelerometer arrays attached to body segments, is described. An array consists of two accelerometers mounted on a rigid rod. The absolute angle of each body segment was determined by band pass filtering of the differences between signals from parallel axes from two accelerometers mounted on the same rod. Joint angles were evaluated by subtracting absolute angles of the neighboring segments. This method eliminates the need for double integration as well as the drift typical for double integration. The efficiency of the algorithm is illustrated by experimental results involving healthy subjects who walked on a treadmill at various speeds, ranging between 0.15 m/s and 2.0 m/s. The validation was performed by comparing the estimated joint angles with the joint angles measured with flexible goniometers. The discrepancies were assessed by the differences between the two sets of data (obtained to be below 6 degrees) and by the Pearson correlation coefficient (greater than 0.97 for the knee angle and greater than 0.85 for the ankle angle).

Sensor-driven four-channel stimulation of paretic leg: functional electrical walking therapy

This study introduces a Functional Electrical Therapy (FET) system based on sensor-driven electrical stimulation for the augmentation of walking. The automatic control relates to the timing of stimulation of four muscles. The sensor system comprises accelerometers and force-sensing resistors. The automatic control implements IF-THEN rules designed by mapping of sensors and muscle activation patterns. The new system was tested in 13 acute stroke patients assigned to a FET group or a control (CON) group. Both groups were treated with a standard rehabilitation program and 45min of walking daily for 5 days over the course of 4 weeks. The FET group received electrical stimulation during walking. The Fugl-Meyer (FM) test for the lower extremities, Barthel Index (BI), mean walking velocity (v(mean)) over a 6-m distance, and Physiological Cost Index (PCI) were assessed at the entry point and at the end of the treatment. Subjects within the FET and CON groups had comparable baseline outcome measures. In the FET group, we determined significant differences in the mean values of all outcomes between the entry and end points of treatment (p<0.05), contrary to the CON group where we found no significant differences (p>0.05). We also found significant differences in the changes of FM, BI, v(mean) and PCI which occurred during the 4 weeks of treatment between the FET and CON groups (p<0.05). The statistical strength of the clinical study was low (<70%), suggesting the need for a larger, randomized clinical trial.

Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients

Alternation of walking pattern decreases quality of life and may result in falls and injuries. Freezing of gait (FOG) in Parkinsons disease (PD) patients occurs occasionally and intermittently, appearing in a random, inexplicable manner. In order to detect typical disturbances during walking, we designed an expert system for automatic classification of various gait patterns. The proposed method is based on processing of data obtained from an inertial sensor mounted on shank. The algorithm separates normal from abnormal gait using Pearsons correlation and describes each stride by duration, shank displacement, and spectral components. A rule-based data processing classifies strides as normal, short (short+) or very short (short-) strides, FOG with tremor (FOG+) or FOG with complete motor block (FOG-). The algorithm also distinguishes between straight and turning strides. In 12 PD patients, FOG+ and FOG- were identified correctly in 100% of strides, while normal strides were recognized in 95% of cases. Short+ and short- strides were identified in about 84% and 78%. Turning strides were correctly identified in 88% of cases. The proposed method may be used as an expert system for detailed stride classification, providing warning for severe FOG episodes and near-fall situations.

Classification of walking patterns in Parkinson's disease patients based on inertial sensor data

The gait disturbances in Parkinsons disease (PD) patients occur occasionally and intermittently, appearing in a random, inexplicable manner. These disturbances include festinations, shuffling, and complete freezing of gait (FOG). Alternation of walking pattern decreases the quality of life and may result in falls. In order to recognize disturbances during walking in PD patients, we recorded gait kinematics with wireless inertial measurement system and designed an algorithm for automatic recognition and classification of walking patterns. The algorithm combines a perceptron neural network with simple signal processing and rule-based classification. In parallel, gait was recorded with video camera. Medical experts identified FOG episodes from videos and their results were used for comparison and validation of this method. The summary result shows that the error in recognition and classification of walking patterns is up to 16%.

Nonlinear optimization for drift removal in estimation of gait kinematics based on accelerometers

A new data processing method is described for estimation of angles of leg segments, joint angles, and trajectories in the sagittal plane from data recorded by sensors units mounted at the lateral side of leg segments. Each sensor unit comprises a pair of three-dimensional accelerometers which send data wirelessly to a PC. The accelerometer signals comprise time-varying and temperature-dependent offset, which leads to drift and diverged signals after integration. The key features of the proposed method are to model the offset by a slowly varying function of time (a cubic spline polynomial) and evaluate the polynomial coefficients by nonlinear numerical simplex optimization with the goal to reduce the drift in processed signals (angles and movement displacements). The angles and trajectories estimated by our method were compared with angles measured by an optical motion capture system. The comparison shows that the errors for angles (rms) were below 4° and the errors in stride length were below 2%. The algorithm developed is applicable for real-time and off-line analysis of gait. The method does not need any adaptation with respect to gait velocity or individuality of gait.

Finger tapping analysis in patients with Parkinson’s disease and atypical parkinsonism

The goal of this study was to investigate repetitive finger tapping patterns in patients with Parkinsons disease (PD), progressive supranuclear palsy-Richardson syndrome (PSP-R), or multiple system atrophy of parkinsonian type (MSA-P). The finger tapping performance was objectively assessed in PD (n=13), PSP-R (n=15), and MSA-P (n=14) patients and matched healthy controls (HC; n=14), using miniature inertial sensors positioned on the thumb and index finger, providing spatio-temporal kinematic parameters. The main finding was the lack or only minimal progressive reduction in amplitude during the finger tapping in PSP-R patients, similar to HC, but significantly different from the sequence effect (progressive decrement) in both PD and MSA-P patients. The mean negative amplitude slope of -0.12°/cycle revealed less progression of amplitude decrement even in comparison to HC (-0.21°/cycle, p=0.032), and particularly from PD (-0.56°/cycle, p=0.001), and MSA-P patients (-1.48°/cycle, p=0.003). No significant differences were found in the average finger separation amplitudes between PD, PSP-R and MSA-P patients (pmsa-pd=0.726, pmsa-psp=0.363, ppsp-pd=0.726). The lack of clinically significant sequence effect during finger tapping differentiated PSP-R from both PD and MSA-P patients, and might be specific for PSP-R. The finger tapping kinematic parameter of amplitude slope may be a neurophysiological marker able to differentiate particular forms of parkinsonism.

Reproducibility of “BUDA” multisensor system for gait analysis

Gait analysis is important element in therapy and rehabilitation of hemiplegic individuals. We developed a simple, portable sensor system, which can be used for recording of gait signals, as well as for estimation of important gait parameters like joint angles, ground reaction forces, and various temporal parameters. In order to assess robustness of the system in everyday use in different environmental conditions and to see how it reacts to atypical gait patterns, we recorded signals from five healthy and five hemiplegic subjects, repeating the experiment on several consecutive days. We compared angles between adjacent leg segments and used Pearsons correlation coefficient as a measure of similarity. We obtained a high correlation for healthy persons, and a notably smaller correlation for hemiplegics.

Quantification of Finger-Tapping Angle Based on Wearable Sensors

We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a dominant axis. The method was verified on twelve subjects, who performed various tapping tasks, mimicking impaired patterns. The obtained tapping angles were compared with results of a motion capture camera system, demonstrating excellent accuracy. The root-mean-square (RMS) error between the two sets of data is, on average, below 4°, and the intraclass correlation coefficient is, on average, greater than 0.972. Data obtained by the proposed method may be used together with scores from clinical tests to enable a better diagnostic. Along with hardware simplicity, this makes the proposed method a promising candidate for use in clinical practice. Furthermore, our definition of the tapping angle can be applied to all tapping assessment systems.

Intra-subject stride-to-stride variability: Selecting subject's representative gait pattern

Stride-to-stride variability is well known and important phenomenon often used for estimation of the gait fluctuations and indicative measure for potential risk of falling in elderly. In many gait analysis studies and the descriptions of new processing and analysis methods, the problem of selecting representative gait cycle is solved with choosing one gait cycle from the middle of the recording sequence, or averaging several cycles from the middle. In this paper, we describe stride-to-stride variability based on data from inertial sensors. We suggest a simple, mathematical method, how to select stride which best represents the subjects gait pattern.