Classification of left and right foot kinaesthetic motor imagery using common spatial pattern

fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface

BACKGROUND

EEG and fMRI have contributed greatly to our understanding of brain activity and its link to behaviors by helping to identify both when and where the activity occurs. This is particularly important in the development of brain-computer interfaces (BCIs), where feed forward systems gather data from imagined brain activity and then send that information to an effector. The purpose of this study was to develop and evaluate a computational approach that enables an accurate mapping of spatial brain activity (fMRI) in relation to the temporal receptors (EEG electrodes) associated with imagined lower limb movement.

NEW METHOD

EEG and fMRI data from 16 healthy, male participants while imagining lower limb movement were used for this purpose. A combined analysis of fMRI data and EEG electrode locations was developed to identify EEG electrodes with a high likelihood of capturing imagined lower limb movement originating from various clusters of brain activity. This novel feature selection tool was used to develop an artificial neural network model to classify right and left lower limb movement.

RESULTS

Results showed that left versus right lower limb imagined movement could be classified with 66.5% accuracy using this approach. Comparison with existing methods: Adopting a purely data-driven approach for feature selection to use in the right/left classification task resulted in the same accuracy (66.6%) but with reduced interpretability.

CONCLUSIONS

The developed fMRI-informed EEG approach could pave the way towards improved brain computer interfaces for lower limb movement while also being applicable to other systems where fMRI could be helpful to inform EEG acquisition and processing.

Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton

Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this study was to assess the laterality of cortical function when performing MI of the lower limb. Brain signals from five subjects were analyzed in two conditions, during exoskeleton-assisted gait and while static. Three different EEG electrode configurations were evaluated: covering both hemispheres, covering the non-dominant hemisphere and covering the dominant hemisphere. In addition, the evolution of performance and laterality with practice was assessed. Although sightly superior results were achieved with information from all electrodes, differences between electrode configurations were not statistically significant. Regarding the evolution during the experimental sessions, the performance of the BCI generally evolved positively the higher the experience was.

Physiologic signaling and viability of the muscle cuff regenerative peripheral nerve interface (MC-RPNI) for intact peripheral nerves

Background. Robotic exoskeleton devices have become a promising modality for restoration of extremity function in individuals with limb loss or functional weakness. However, there exists no consistent or reliable way to record efferent motor action potentials from intact peripheral nerves to control device movement. Peripheral nerve motor action potentials are similar in amplitude to that of background noise, producing an unfavorable signal-to-noise ratio (SNR) that makes these signals difficult to detect and interpret. To address this issue, we have developed the muscle cuff regenerative peripheral nerve interface (MC-RPNI), a construct consisting of a free skeletal muscle graft wrapped circumferentially around an intact peripheral nerve. Over time, the muscle graft regenerates, and the intact nerve undergoes collateral axonal sprouting to reinnervate the muscle. The MC-RPNI amplifies efferent motor action potentials by several magnitudes, thereby increasing the SNR, allowing for higher fidelity signaling and detection of motor intention. The goal of this study was to characterize the signaling capabilities and viability of the MC-RPNI over time.Methods. Thirty-seven rats were randomly assigned to one of five experimental groups (Groups A-E). For MC-RPNI animals, their contralateral extensor digitorum longus (EDL) muscle was harvested and trimmed to either 8 mm (Group A) or 13 mm (Group B) in length, wrapped circumferentially around the intact ipsilateral common peroneal (CP) nerve, secured, and allowed to heal for 3 months. Additionally, one 8 mm (Group C) and one 13 mm (Group D) length group had an epineurial window created in the CP nerve immediately preceding MC-RPNI creation. Group E consisted of sham surgery animals. At 3 months, electrophysiologic analyses were conducted to determine the signaling capabilities of the MC-RPNI. Additionally, electromyography and isometric force analyses were performed on the CP-innervated EDL to determine the effects of the MC-RPNI on end organ function. Following evaluation, the CP nerve, MC-RPNI, and ipsilateral EDL muscle were harvested for histomorphometric analysis.Results. Study endpoint analysis was performed at 3 months post-surgery. All rats displayed visible muscle contractions in both the MC-RPNI and EDL following proximal CP nerve stimulation. Compound muscle action potentials were recorded from the MC-RPNI following proximal CP nerve stimulation and ranged from 3.67 ± 0.58 mV to 6.04 ± 1.01 mV, providing efferent motor action potential amplification of 10-20 times that of a normal physiologic nerve action potential. Maximum tetanic isometric force (F_o) testing of the distally-innervated EDL muscle in MC-RPNI groups producedF_o(2341 ± 114 mN-2832 ± 102 mN) similar to controls (2497 ± 122 mN), thus demonstrating that creation of MC-RPNIs did not adversely impact the function of the distally-innervated EDL muscle. Overall, comparison between all MC-RPNI sub-groups did not reveal any statistically significant differences in signaling capabilities or negative effects on distal-innervated muscle function as compared to the control group.Conclusions. MC-RPNIs have the capability to provide efferent motor action potential amplification from intact nerves without adversely impacting distal muscle function. Neither the size of the muscle graft nor the presence of an epineurial window in the nerve had any significant impact on the ability of the MC-RPNI to amplify efferent motor action potentials from intact nerves. These results support the potential for the MC-RPNI to serve as a biologic nerve interface to control advanced exoskeleton devices.

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

Neurofeedback (NF) in combination with motor imagery (MI) can be used for training individuals to volitionally modulate sensorimotor activity without producing overt movements. However, until now, NF methods were of limited utility for mentally training specific hand and finger actions. Here we employed a novel transcranial magnetic stimulation (TMS) based protocol to probe and detect MI-induced motor activity patterns in the primary motor cortex (M1) with the aim to reinforce selective facilitation of single finger representations. We showed that TMS-NF training but not MI training with uninformative feedback enabled participants to selectively upregulate corticomotor excitability of one finger, while simultaneously downregulating excitability of other finger representations within the same hand. Successful finger individuation during MI was accompanied by strong desynchronization of sensorimotor brain rhythms, particularly in the beta band, as measured by electroencephalography. Additionally, informative TMS-NF promoted more dissociable EEG activation patterns underlying single finger MI, when compared to MI of the control group where no such feedback was provided. Our findings suggest that selective TMS-NF is a new approach for acquiring the ability of finger individuation even if no overt movements are performed. This might offer new treatment modality for rehabilitation after stroke or spinal cord injury.

Enhancement of Event-Related Desynchronization in Motor Imagery Based on Transcranial Electrical Stimulation

Due to the individual differences controlling brain-computer interfaces (BCIs), the applicability and accuracy of BCIs based on motor imagery (MI-BCIs) are limited. To improve the performance of BCIs, this article examined the effect of transcranial electrical stimulation (tES) on brain activity during MI. This article designed an experimental paradigm that combines tES and MI and examined the effects of tES based on the measurements of electroencephalogram (EEG) features in MI processing, including the power spectral density (PSD) and dynamic event-related desynchronization (ERD). Finally, we investigated the effect of tES on the accuracy of MI classification using linear discriminant analysis (LDA). The results showed that the ERD of the μ and β rhythms in the left-hand MI task was enhanced after electrical stimulation with a significant effect in the tDCS group. The average classification accuracy of the transcranial alternating current stimulation (tACS) group and transcranial direct current stimulation (tDCS) group (88.19% and 89.93% respectively) were improved significantly compared to the pre-and pseudo stimulation groups. These findings indicated that tES can improve the performance and applicability of BCI and that tDCS was a potential approach in regulating brain activity and enhancing valid features during noninvasive MI-BCI processing.

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

Brain-computer interfaces (BCI) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation.

OBJECTIVE

to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar. This system will overcome the limitation of existing systems.

APPROACH

This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this 4 sessions study across 4 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 RLDA classifiers that used the µ power spectral density (PSD) 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 was presented to half of the participants.

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 modified feedback 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 the first novel integration of different design approaches, different control modes and different performance enhancement techniques, all in parallel in one single high performance and multi-modal BCI system, to control single steps and forward walking of an immersive virtual reality avatar.

Improving performance in motor imagery BCI-based control applications via virtually embodied feedback

OBJECTIVE

Brain-computer interfaces (BCIs) based on motor imagery (MI) are commonly used for control applications. However, these applications require strong and discriminant neural patterns for which extensive experience in MI may be necessary. Inspired by the field of rehabilitation where embodiment is a key element for improving cortical activity, our study proposes a novel control scheme in which virtually embodiable feedback is provided during control to enhance performance.

METHODS

Subjects underwent two immersive virtual reality control scenarios in which they controlled the two-dimensional movement of a device using electroencephalography (EEG). The two scenarios only differ on whether embodiable feedback, which mirrors the movement of the classified intention, is provided. After undergoing each scenario, subjects also answered a questionnaire in which they rated how immersive the scenario and embodiable the feedback were.

RESULTS

Subjects exhibited higher control performance, greater discriminability in brain activity patterns, and enhanced cortical activation when using our control scheme compared to the standard control scheme in which embodiable feedback is absent. Moreover, the self-rated embodiment and presence scores showed significantly positive linear relationships with performance.

SIGNIFICANCE

The findings in our study provide evidence that providing embodiable feedback as guidance on how intention is classified may be effective for control applications by inducing enhanced neural activity and patterns with greater discriminability. By applying embodiable feedback to immersive virtual reality, our study also serves as another instance in which virtual reality is shown to be a promising tool for improving MI.