Graduate Degree Type
Dr. Samhita Rhodes
Dr. Gordon Alderink
Dr. Yunju Lee
In quiet standing the central nervous systems implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy is comprised of a synchronized common neural drive being delivered to synergistically grouped muscles. In this study connectivity between EMG signals of unilateral and bilateral homologous muscle pairs, of the lower legs, during various standing balance conditions was evaluated using magnitude squared coherence (MSC) and mutual information (MI). The leg muscles of interest were the tibialis anterior (TA), medial gastrocnemius (MG), and the soleus (S) of both legs. MSC is a linear measure of the phase relation between two signals in the frequency domain. MI is an information theoretic measure of the amount of information two signals have in common. Both MSC and MI were analyzed in the delta (0.5 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 13 Hz), beta (13 – 30 Hz), and gamma (30 – 100 Hz) neural frequency bands for feet together and feet tandem, with eyes open and eyes closed conditions. Both MSC and MI found that overall connectivity was highest in the delta band followed by the theta band. Connectivity in the beta and lower gamma bands (30 – 60 Hz) was influenced by standing balance condition and indicative of a neural drive originating from the motor cortex. Instability was evaluated by comparing less stable standing conditions with a baseline eyes open, feet together stance. Changes in connectivity in the beta and gamma bands were found be most significant in the muscle pairs of the back leg of tandem stance regardless of foot dominance. MI was found to be a better connectivity analysis method by identifying significance of increased connectivity in the agonistic muscle pair between the MG:S, the antagonistic muscle pair between TA:S, and all the bilateral homologous muscle pairs. MSC was only able to identify the MG:S muscle pair as significant. The results of this study provided insight into the neural mechanism of postural control and presented an alternative connectivity analysis method of MI.
McCrumb, Diana, "Analysis of Connectivity in EMG Signals to Examine Neural Correlations in Muscular Activation of Lower Leg Muscles for Postural Stability" (2019). Masters Theses. 959.