In 2015 the National Spinal Cord Injury Association of Canada reported that 30,000 Canadians suffer from paralysis in two or more limbs. In many cases this takes away the fundamental ability to walk. Walking, an intricate sensorimotor task, involves the interactions of both dynamic and balancing neurological processes. Brain computer interfaces (BCIs) are attempting to bridge the gap that will allow persons with compromised mobility to interact with the world via control of prosthetic devices that can ‘act’ by using solely neural input (i.e. thoughts). The goal of this thesis was to aid in the development of a BCI for lower limb locomotion by identifying similarities and differences between cortical activity associated with executed and imagined left and right lower limb movements using electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI). Data from 16 participants showed that it was possible to differentiate between right versus left executed and imagined thought processes for lower limb locomotion using solely information from an EEG, and that these patterns of brain activity were generalizable across time points and trials. It was also found, through the use of fMRI, that areas of brain activation in executed and imagined conditions were similar for some areas but showed unique activation areas as well. A novel paradigm to co-register EEG and fMRI data was developed that can easily be utilized in other contexts. Finally, using EEG and fMRI data allowed for an efficient model to use in a machine learning paradigm that successfully predicted left versus right lower limb movement. This research adds to the existing body of knowledge in understanding psychomotor brain activity associated with thought coordination processes involved in the task of walking in normal persons represented by algorithmic patterns.