Eye Gaze Correlates of Motor Impairment in VR Observation of Motor Actions.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Methodologies, Models and Algorithms for Patients Rehabilitation". OBJECTIVE: Identify eye gaze correlates of motor impairment in a virtual reality motor observation task in a study with healthy participants and stroke patients. METHODS: Participants consisted of a group of healthy subjects (N = 20) and a group of stroke survivors (N = 10). Both groups were required to observe a simple reach-and-grab and place-and-release task in a virtual environment. Additionally, healthy subjects were required to observe the task in a normal condition and a constrained movement condition. Eye movements were recorded during the observation task for later analysis. RESULTS: For healthy participants, results showed differences in gaze metrics when comparing the normal and arm-constrained conditions. Differences in gaze metrics were also found when comparing dominant and non-dominant arm for saccades and smooth pursuit events. For stroke patients, results showed longer smooth pursuit segments in action observation when observing the paretic arm, thus providing evidence that the affected circuitry may be activated for eye gaze control during observation of the simulated motor action. CONCLUSIONS: This study suggests that neural motor circuits are involved, at multiple levels, in observation of motor actions displayed in a virtual reality environment. Thus, eye tracking combined with action observation tasks in a virtual reality display can be used to monitor motor deficits derived from stroke, and consequently can also be used for rehabilitation of stroke patients.

Visualization of multivariate physiological data for cardiorespiratory fitness assessment through ECG (R-peak) analysis.

The recent rise and popularization of wearable and ubiquitous fitness sensors has increased our ability to generate large amounts of multivariate data for cardiorespiratory fitness (CRF) assessment. Consequently, there is a need to find new methods to visualize and interpret CRF data without overwhelming users. Current visualizations of CRF data are mainly tabular or in the form of stacked univariate plots. Moreover, normative data differs significantly between gender, age and activity, making data interpretation yet more challenging. Here we present a CRF assessment tool based on radar plots that provides a way to represent multivariate cardiorespiratory data from electrocardiographic (ECG) signals within its normative context. To that end, 5 parameters are extracted from raw ECG data using R-peak information: mean HR, SDNN, RMSSD, HRVI and the maximal oxygen uptake, VO2max. Our tool processes ECG data and produces a visualization of the data in a way that it is easy to compare between the performance of the user and normative data. This type of representation can assist both health professionals and non-expert users in the interpretation of CRF data.

A functional magnetic resonance imaging study of visuomotor processing in a virtual reality-based paradigm: Rehabilitation Gaming System.

The Rehabilitation Gaming System (RGS) has been designed as a flexible, virtual-reality (VR)-based device for rehabilitation of neurological patients. Recently, training of visuomotor processing with the RGS was shown to effectively improve arm function in acute and chronic stroke patients. It is assumed that the VR-based training protocol related to RGS creates conditions that aid recovery by virtue of the human mirror neuron system. Here, we provide evidence for this assumption by identifying the brain areas involved in controlling the catching of approaching colored balls in the virtual environment of the RGS. We used functional magnetic resonance imaging of 18 right-handed healthy subjects (24 ± 3 years) in both active and imagination conditions. We observed that the imagery of target catching was related to activation of frontal, parietal, temporal, cingulate and cerebellar regions. We interpret these activations in relation to object processing, attention, mirror mechanisms, and motor intention. Active catching followed an anticipatory mode, and resulted in significantly less activity in the motor control areas. Our results provide preliminary support for the hypothesis underlying RGS that this novel neurorehabilitation approach engages human mirror mechanisms that can be employed for visuomotor training.

Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training.

Stroke is one of the leading causes of adult disability with high economical and societal costs. In recent years, novel rehabilitation paradigms have been proposed to address the life-long plasticity of the brain to regain motor function. We propose a hybrid brain-computer interface (BCI)-virtual reality (VR) system that combines a personalized motor training in a VR environment, exploiting brain mechanisms for action execution and observation, and a neuro-feedback paradigm using mental imagery as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracts. Furthermore, we present the development and validation experiments of the proposed system. More specifically, EEG data on nine naïve healthy subjects show that a simultaneous motor activity and motor imagery paradigm is more effective at engaging cortical motor areas and related networks to a larger extent. Additionally, we propose a motor imagery driven BCI-VR version of our system that was evaluated with nine different healthy subjects. Data show that users are capable of controlling a virtual avatar in a motor imagery training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system.

Real-time position reconstruction with hippocampal place cells.

Brain-computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.