Improving motor imagery through a mirror box for BCI users.

The aim of this study was to evaluate mirror visual feedback (MVF) as a training tool for brain-computer interface (BCI) users. This is because approximately 20-30% of subjects require more training to operate a BCI system using motor imagery. Electroencephalograms (EEGs) were recorded from 18 healthy subjects, using event-related desynchronization (ERD) to observe the responses during the movement or movement intention of the hand for the conditions of control, imagination, and the MVF with the mirror box. We constituted two groups: group 1: control, imagination, and MVF; group 2: control, MVF, and imagination. There were significant differences in imagination conditions between groups using MVF before or after imagination (right-hand, P = 0.0403; left-hand, P = 0.00939). The illusion of movement through MVF is not possible in all subjects, but even in those cases, we found an increase in imagination when the subject used the MVF previously. The increase in the r2s of imagination in the right and left hands suggests cross-learning. The increase in motor imagery recorded with EEG after MVF suggests that the mirror box made it easier to imagine movements. Our results provide evidence that the MVF could be used as a training tool to improve motor imagery.NEW & NOTEWORTHY The increase in motor imagery recorded with EEG after MVF (mirror visual feedback) suggests that the mirror box made it easier to imagine movements. Our results demonstrate that MVF could be used as a training tool to improve motor imagery.

Estimation of muscle forces in gait using a simulation of the electromyographic activity and numerical optimization.

Clinical gait analysis provides great contributions to the understanding of gait patterns. However, a complete distribution of muscle forces throughout the gait cycle is a current challenge for many researchers. Two techniques are often used to estimate muscle forces: inverse dynamics with static optimization and computer muscle control that uses forward dynamics to minimize tracking. The first method often involves limitations due to changing muscle dynamics and possible signal artefacts that depend on day-to-day variation in the position of electromyographic (EMG) electrodes. Nevertheless, in clinical gait analysis, the method of inverse dynamics is a fundamental and commonly used computational procedure to calculate the force and torque reactions at various body joints. Our aim was to develop a generic musculoskeletal model that could be able to be applied in the clinical setting. The musculoskeletal model of the lower limb presents a simulation for the EMG data to address the common limitations of these techniques. This model presents a new point of view from the inverse dynamics used on clinical gait analysis, including the EMG information, and shows a similar performance to another model available in the OpenSim software. The main problem of these methods to achieve a correct muscle coordination is the lack of complete EMG data for all muscles modelled. We present a technique that simulates the EMG activity and presents a good correlation with the muscle forces throughout the gait cycle. Also, this method showed great similarities whit the real EMG data recorded from the subjects doing the same movement.