Background/Objectives: Precise stepping control is fundamental to human mobility, and impairments increase fall risk in older adults and individuals with neurological conditions. This study investigated the cortical networks underlying stepping accuracy using mobile brain/body imaging with electroencephalography (EEG)-based exact low-resolution electromagnetic tomography-independent component analysis (eLORETA-ICA) and microstate segmentation analysis (MSA). Methods: Sixteen healthy male participants performed a precision stepping task while wearing a mobile EEG system. Step performance was quantified using error distance, measuring deviation between target and heel contact points. Preprocessed EEG data were analyzed using eLORETA-ICA and MSA, with participants categorized into high- and low-performing groups. Results: Seven microstate clusters were identified, with the anterior cingulate cortex (ACC) showing the highest microstate probability (21.15%). The high-performing group exhibited amplified theta-band activity in the ACC, enhanced activity in the precuneus and postcentral gyrus, and suppressed mu- and beta-band activity in the paracentral lobules. Conclusions: Stepping accuracy relies on a distributed neural network, with the ACC playing a central role in performance monitoring. We propose an integrated framework comprising the following systems: error monitoring (ACC), sensorimotor integration (paracentral lobules), and visuospatial processing (precuneus and occipital regions). These findings highlight the importance of neural oscillatory mechanisms in precise motor control and offer insights for rehabilitation strategies and fall prevention programs.

Background: Jump-landing is a fundamental movement critical for enhancing athletic performance and preventing injuries, making the facilitation of rapid motor learning essential. Motor learning and performance are commonly evaluated using biomechanical measures. Although neurophysiological processes such as predictive control and self-reflection are thought to contribute to motor learning, studies from this perspective remain limited. In this study, we focused on three neural markers: Bereitschaftspotential (BP), which reflects predictive control before movement initiation; posterior parietal cortex (PPC) activity, which is involved in sensory information processing during motor learning; and error-related negativity (ERN), which reflects self-reflection following movement. We aimed to clarify the relationships between these neural markers and motor learning during jump-landing tasks. Methods: A cross-sectional study was conducted with eight healthy male participants, each performing twenty single-leg drop jumps. Participants were instructed to land on a designated target point, and the error distance between the big toe and the target was measured. Reduction in error distance across trials was quantified as a learning curve, and its slope was used as an index of motor learning ability. Bereitschaftspotential (BP) was measured at the Cz electrode, and activity in the posterior parietal cortex (PPC) was analyzed at the Pz electrode; integral values over the three seconds prior to jump takeoff were calculated. ERN was extracted from the Fz electrode as the maximum negative amplitude occurring 50-150 ms after landing. Statistical analyses were conducted to examine the correlations between electroencephalography indices and the learning curve slope. In addition, classification using a support vector machine (SVM) was performed to assess whether ERN amplitude could predict high or low motor learning ability. Results: BP and PPC activity were significantly negatively correlated with the learning curve slope, indicating faster motor learning. In contrast, ERN amplitude showed no significant correlation with the slope. However, the SVM classification model demonstrated that ERN amplitude could accurately predict high and low motor learning ability. Conclusion: BP and PPC activity contributed to faster motor learning, while ERN enabled classification of learning ability. These findings suggest that predictive control, sensory integration, and self-reflection are key components of motor learning. This study is among the first to integratively examine the roles of BP, PPC, and ERN in a dynamic jump-landing. The findings demonstrate that predictive control, sensory integration, and self-reflection are key contributors to motor learning efficiency. These insights offer novel perspectives for assessment and training design in sports science and rehabilitation, with implications for performance enhancement and injury prevention.