The goal of this investigation was to explore the efficacy of implementing a rehabilitation robot controlled by a noninvasive brain-computer interface (BCI) to influence brain plasticity and facilitate motor learning. The motivation of this project stemmed from the need to address the population of stroke survivors who have few or no options for therapy.
A BCI is a system or device that detects minute changes in brain signals to facilitate communication or control. In this investigation, the BCI was implemented through an electroencephalograph (EEG) device. EEG devices detect electrical brain signals transmitted through the scalp that corresponded with imagined motor activity. Within the BCI, a linear transformation algorithm converted EEG spectral features into control commands for an upper-limb rehabilitative robot, thus implementing a closed-looped feedback-control training system. The concept of the BCI-robot system implemented in this investigation may provide an alternative to current therapies by demonstrating the results of bypassing motor activity using brain signals to facilitate robotic therapy.
In this study, 24 able-bodied volunteers were divided into two study groups; one group trained to use sensorimotor rhythms (SMRs) (produced by imagining motor activity) to control the movement of a robot and the other group performed the ‘guided-imagery’ task of watching the robot move without control. This investigation looked for contrasts between the two groups that showed that the training involved with controlling the BCI-robot system had an effect on brain plasticity and motor learning.
To analyze brain plasticity and motor learning, EEG data corresponding to imagined arm movement and motor learning were acquired before, during, and after training. Features extracted from the EEG data consisted of frequencies in the 5-35Hz range, which produced amplitude fluctuations that were measurably significant during reaching. Motor learning data consisted of arm displacement measures (error) produced during an motor adaptation task performed daily by all subjects.
The results of the brain plasticity analysis showed persistent reductions in beta activity for subjects in the BCI group. The analysis also showed that subjects in the Non-BCI group had significant reductions in mu activity; however, these results were likely due to the fact that different EEG caps were used in each stage of the study. These results were promising but require further investigation.
The motor learning data showed that the BCI group out-performed non-BCI group in all measures of motor learning. These findings were significant because this was the first time a BCI had been applied to a motor learning protocol and the findings suggested that BCI had an influence on the speed at which subjects adapted to a motor learning task. Additional findings suggested that BCI subjects who were in the 40 and over age group had greater decreases in error after the learning phase of motor assessment. These finding suggests that BCI could have positive long term effects on individuals who are more likely to suffer from a stroke and possibly could be beneficial for chronic stroke patients.
In addition to exploring the effects of BCI training on brain plasticity and motor learning this investigation sought to detect whether the EEG features produced during guided-imagery could differentiate between reaching direction. While the analysis presented in this project produced classification accuracies no greater than ∼77%, it formed the basis of future studies that would incorporate different pattern recognition techniques. (Abstract shortened by UMI.)