Electroencephalography (EEG) is the aggregate of electrical signals emitted from the brain. Study of EEG has been widely used as a useful noninvasive method of collecting bioelectric signals from humans. Processing and modeling of bioelectric signals allows us to understand the fundamental nature of human behavior. The relationship between EEG and brain functions can be related to their frequency and spatial patterns. These properties are relatively unknown in infants during the first year of life. During this time period, infants typically acquire the ability to crawl. This skill is important in developing necessary motor skills for future locomotion as well as primary cognitive abilities for interacting with their environment. Infants with cerebral palsy (CP) experience severe delays in acquisition of locomotive skills, which hinders them throughout the rest of their lives. Our interdisciplinary group has designed a Self-Initiated Prone Progression Crawler (SIPPC) locomotion assistive robot to aid infants in developing crawling patterns. In this discussion, results from an ongoing study of EEG data collected from typically developing infants using the SIPPC robot are presented. Weekly collection of resting EEG data prior to crawling are studied longitudinally to understand infants’ progression developmentally and assess neural effects from varying degrees of robot assistance.