3D visualization of movements can amplify motor cortex activation during subsequent motor imagery.

Teresa Sollfrank,Tele Tan,Daniel Hart,Rachel Goodsell,Jonathan Foster

doi:10.3389/fnhum.2015.00463

Abstract

A repetitive movement practice by motor imagery (MI) can influence motor cortical excitability in the electroencephalogram (EEG). This study investigated if a realistic visualization in 3D of upper and lower limb movements can amplify motor related potentials during subsequent MI. We hypothesized that a richer sensory visualization might be more effective during instrumental conditioning, resulting in a more pronounced event related desynchronization (ERD) of the upper alpha band (10–12 Hz) over the sensorimotor cortices thereby potentially improving MI based brain-computer interface (BCI) protocols for motor rehabilitation. The results show a strong increase of the characteristic patterns of ERD of the upper alpha band components for left and right limb MI present over the sensorimotor areas in both visualization conditions. Overall, significant differences were observed as a function of visualization modality (VM; 2D vs. 3D). The largest upper alpha band power decrease was obtained during MI after a 3-dimensional visualization. In total in 12 out of 20 tasks the end-user of the 3D visualization group showed an enhanced upper alpha ERD relative to 2D VM group, with statistical significance in nine tasks.With a realistic visualization of the limb movements, we tried to increase motor cortex activation during subsequent MI. The feedback and the feedback environment should be inherently motivating and relevant for the learner and should have an appeal of novelty, real-world relevance or aesthetic value (Ryan and Deci, 2000; Merrill, 2007). Realistic visual feedback, consistent with the participant’s MI, might be helpful for accomplishing successful MI and the use of such feedback may assist in making BCI a more natural interface for MI based BCI rehabilitation.

Highlights

Over the past several years, advances in the analysis of electroencephalogram (EEG) signals and improved computing capabilities have enabled people with severe motor disabilities to use their own brain activity for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system (Kübler et al, 2001; Wolpaw et al, 2002; Allison et al, 2007; Perdikis et al, 2014)
The current study investigated if a 3-dimensional visualization of five different upper and lower limb movements could amplify motor cortex activation during subsequent motor imagery (MI) and thereby give prospective support for the use of a Sensorimotor rhythms (SMRs) based brain-computer interface (BCI)
A repeated measures ANOVA was performed on the event related desynchronization (ERD)/ERS data using the visualization modality (VM) (2 levels: 2D vs. 3D), task (5 levels: wrist movement, elbow rotation, arm flexion, knee and ankle rotation), electrode position (EP) (2 levels: C3 vs. C4) and task side (2 levels: left vs. right) as withinsubjects variables, in order to analyze the potential influence of the VM on the ERD patterns during MI

Summary

Introduction

Over the past several years, advances in the analysis of electroencephalogram (EEG) signals and improved computing capabilities have enabled people with severe motor disabilities to use their own brain activity for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system (Kübler et al, 2001; Wolpaw et al, 2002; Allison et al, 2007; Perdikis et al, 2014). Individual user’s characteristics, such as psychosocial and physiological parameters (e.g., fine motor skills) or brain structures, can predict performances for SMR-based BCIs (Blankertz et al, 2010; Halder et al, 2011; Hammer et al, 2012; Randolph, 2012). Besides these factors, feedback is a necessary feature for initial learning to modulate the sensorimotor rhythm (Wolpaw et al, 1991, 2002; McFarland et al, 1998). The end-user have to be properly trained to be able to successfully control their EEG signals, especially for the use of a BCI based on the recognition of mental imagery tasks (e.g., MI; Neuper and Pfurtscheller, 2001)

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Conclusion