BCI-FES With Multimodal Feedback for Motor Recovery Poststroke

An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals-user-generated intent-to-move neural activity recorded from cerebral cortical motor areas-to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity.

Time to Capture a Moving Target Travelling along a Circular Trajectory

This study measured the time it took to select a target moving along a circular trajectory with a computer mouse. The time was changed according to the speed of the target, the width of target and the distance from the starting point to the target. However, the effect of these independent variables on the dependent variable was different from what was expected. In the previous studies, it was assumed that the faster the moving target speed, the longer the target selection time, because increased target speed had the effect of narrowing the effective target width. However, as a result of the experiment, the target selection time was rather shortened when the moving speed of the target was increased. This may be because the subjects intend to speed up target selection while decreasing the accuracy of target selection in order to adapt to a fast-moving target. The modified Fitts’ model for the moving target selection time proposed in a previous study did not take these user responses into account. A more modified model is required to more accurately describe the selection time of moving target.

Accuracy of Dynamic Force Compensation Varies With Direction and Speed

This study investigates physical responses to force perturbations while tracking a moving target. The results show accuracy depends on the direction of a force perturbation and speed of the task, but generally not on hand. There are also differences in responses when the force is first applied and when it is removed.

Pointing Device Performance in Steering Tasks

Use of touch-screen-based interactions is growing rapidly. Hence, knowing the maneuvering efficacy of touch screens relative to other pointing devices is of great importance in the context of graphical user interfaces. Movement time, accuracy, and user preferences of four pointing device settings were evaluated on a computer with 14 participants aged 20.1 ± 3.13 years. It was found that, depending on the difficulty of the task, the optimal settings differ for ballistic and visual control tasks. With a touch screen, resting the arm increased movement time for steering tasks. When both performance and comfort are considered, whether to use a mouse or a touch screen for person–computer interaction depends on the steering difficulty. Hence, a input device should be chosen based on the application, and should be optimized to match the graphical user interface.

Linking brain stroke risk factors to human movement features for the development of preventive tools

This paper uses human movement analyses to assess the susceptibility of brain stroke, one of the most important causes of disability in elders. To that end, a computerized battery of nine neuromuscular tests has been designed and evaluated with a sample of 120 subjects with or without stoke risk factors. The kinematics of the movements produced was analyzed using a computational neuromuscular model and predictive characteristics were extracted. Logistic regression and linear discriminant analysis with leave-one-out cross-validation was used to infer the probability of presence of brain stroke risk factors. The clinical potential value of movement information for stroke prevention was assessed by computing area under the receiver operating characteristic curve (AUC) for the diagnostic of risk factors based on motion analysis. AUC mostly varying between 0.6 and 0.9 were obtained, depending on the neuromuscular test and the risk factor investigated (obesity, diabetes, hypertension, hypercholesterolemia, cigarette smoking, and cardiac disease). Our results support the feasibility of the proposed methodology and its potential application for the development of brain stroke prevention tools. Although further research is needed to improve this methodology and its outcome, results are promising and the proposed approach should be of great interest for many experimenters open to novel approaches in preventive medicine and in gerontology. It should also be valuable for engineers, psychologists, and researchers using human movements for the development of diagnostic and neuromuscular assessment tools.

Environmental context affects outcome and kinematic changes at different rates during skill learning

Based on Gentile's learning model, this study used a dart-throwing task to investigate the influence of environmental context. Novice participants (N = 32) were trained in one of four conditions, while measuring outcomes and kinematics. The interaction of regulatory conditions (stationary/in motion) and intertrial variability (present/absent) created four target conditions: (1) stationary with one location, (2) stationary with five locations, (3) moving with one movement pattern, (4) moving with five starting locations. Performance outcome (radial error) and movement coordination (displacement of shoulder, elbow, and wrist) changes were investigated during three days of practice (480 trials). Radial error scores were analyzed using a 3 x 8 x 4 (Day x Trial Block x Condition) analysis of variance, repeated measures design. The transformed cross-correlation values of the kinematic trials were analyzed using a 3 x 3 x 4 (Joint x Day x Condition) analysis of variance, repeated measures design. Reducing the environmental context complexity of the skill (closed regulatory conditions and no inter-trial variability), decreased outcome errors and changed kinematics at different times in the learning. The environmental context influence was observed by a day x condition interaction on joint coordination. Inter-trial variability had its greatest influence on coordination. The environmental context should be taken into consideration when evaluating and assessing skill performance during learning.

Agonistic and antagonistic interaction in speed/accuracy tradeoff: a delta-lognormal perspective

This paper reports the results of a model-based analysis of movements gathered in a 4×4 experimental design of speed/accuracy tradeoffs with variable target distances and width. Our study was performed on a large (120 participants) and varied sample (both genders, wide age range, various health conditions). The delta-lognormal equation was used for data modeling to investigate the interaction between the output of the agonist and the antagonist neuromuscular systems. Empirical observations show that the subjects must correlate more tightly the impulse commands sent to both neuromuscular systems in order to achieve good performances as the difficulty of the task increases whereas the correlation in the timing of the neuromuscular action co-varies with the size of the geometrical properties of the task. These new phenomena are discussed under the paradigm provided by the Kinematic Theory and new research hypotheses are proposed for further investigation of the speed/accuracy tradeoffs.