Effects of Sensorimotor Rhythm Modulation on the Human Flexor Carpi Radialis H-Reflex

People can learn over training sessions to increase or decrease sensorimotor rhythms (SMRs) in the electroencephalogram (EEG). Activity-dependent brain plasticity is thought to guide spinal plasticity during motor skill learning; thus, SMR training may affect spinal reflexes and thereby influence motor control. To test this hypothesis, we investigated the effects of learned mu (8–13 Hz) SMR modulation on the flexor carpi radialis (FCR) H-reflex in 6 subjects with no known neurological conditions and 2 subjects with chronic incomplete spinal cord injury (SCI). All subjects had learned and practiced over more than 10 < 30-min training sessions to increase (SMR-up trials) and decrease (SMR-down trials) mu-rhythm amplitude over the hand/arm area of left sensorimotor cortex with ≥80% accuracy. Right FCR H-reflexes were elicited at random times during SMR-up and SMR-down trials, and in between trials. SMR modulation affected H-reflex size. In all the neurologically normal subjects, the H-reflex was significantly larger [116% ± 6 (mean ± SE)] during SMR-up trials than between trials, and significantly smaller (92% ± 1) during SMR-down trials than between trials (p < 0.05 for both, paired t-test). One subject with SCI showed similar H-reflex size dependence (high for SMR-up trials, low for SMR-down trials): the other subject with SCI showed no dependence. These results support the hypothesis that SMR modulation has predictable effects on spinal reflex excitability in people who are neurologically normal; they also suggest that it might be used to enhance therapies that seek to improve functional recovery in some individuals with SCI or other CNS disorders

A Novel Dry Electrode for Brain-Computer Interface

A brain-computer interface is a device that uses signals recorded from the brain to directly control a computer. In the last few years, P300-based brain-computer interfaces (BCIs) have proven an effective and reliable means of communication for people with severe motor disabilities such as amyotrophic lateral sclerosis (ALS). Despite this fact, relatively few individuals have benefited from currently available BCI technology. Independent BCI use requires easily acquired, good-quality electroencephalographic (EEG) signals maintained over long periods in less-than-ideal electrical environments. Conventional, wet-sensor, electrodes require careful application. Faulty or inadequate preparation, noisy environments, or gel evaporation can result in poor signal quality. Poor signal quality produces poor user performance, system downtime, and user and caregiver frustration. This study demonstrates that a hybrid dry electrode sensor array (HESA) performs as well as traditional wet electrodes and may help propel BCI technology to a widely accepted alternative mode of communication