A multi-modal modified feedback self-paced BCI to control the gait of an avatar

Abstract

Brain–computer interfaces (BCIs) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation. Some limitations of existing systems are: (a) some systems use mental imagery (MI) of movements other than gait; (b) most systems allow the user to take single steps or to walk but do not allow both; (c) most function in a single BCI mode (cue-paced or self-paced). Objective. The objective of this study was to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar. Approach. This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this study, which was comprised of four sessions across four different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated regularized linear discriminate analysis classifiers that used the µ power spectral density over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback (MDF) was presented to half of the participants, where the avatar moved correctly regardless of the classification of the participants’ MI. The performance of the BCI was computed on each day, using different control modes. Main results. All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9 ± 8.4% showing that MDF enhanced BCI performance (p = 0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode. Significance. This study reports on a first BCI to use motor imagery of the lower limbs in order to control the gait of an avatar with different control modes and different control commands (single steps or forward walking). BCI performance is increased in a novel way by combining three different performance enhancement techniques, resulting in a single high performance and multi-modal BCI system. This study also showed that the improvements due to the effects of MDF lasted for more than one session.

Proprioception enhancement for robot assisted neural rehabilitation: a dynamic electrical stimulation based method and preliminary results from EEG analysis

“…It can be seen from the result of the PSD analysis, the PSD topographical maps show bilateral activation, which is similar to the results given in [30,31]. A possible explanation is that, since the sensorimotor neurons associated with the lower limb movement are located in the central area of central sulcus, the cortical activation observed during the unilateral limb movement is usually bilateral [32].…”

Proprioceptive Stimulation Added to a Walking Self-Avatar Enhances the Illusory Perception of Walking in Static Participants

“…Indeed, sway was increased when the visual and proprioceptive information were congruent. Future work will include combining the proprioceptive stimulation with a braincomputer interface (BCI) that controls the gait of the avatar (Alchalabi et al, 2021), in an effort to produce a stronger sense of agency and test its application for motor rehabilitation.…”

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

“…Reust and his colleagues employed an MI-BCI system corresponding to human hand movement to control two robotic hands; this approach achieved a 95% classification accuracy overall (Reust et al, 2018 ). Another novel mental imagery system developed by the University of Montreal employed a multimodal BCI system to control the single-step and forward walking status using an immersive virtual reality avatar (Alchalabi et al, 2021 ).…”