The field of neuroeconomics has begun to identify neural mechanisms underlying self-controlled behavior; however, researchers have not yet developed interventions that directly target these mechanisms in order to improve self-control. Using EEG operant conditioning in conjunction with a well-validated source localization algorithm (standardized low-resolution electromagnetic tomography; sLORETA), we trained dieting subjects to up-regulate neural activity in brain regions that have been linked to self-control. The training led to neural activity changes, and to corresponding reductions in consumption of high-sugar and high-fat foods, which were operationalized as postprandial blood glucose and triglyceride levels, respectively. Text messaging was used to collect self-report data on craving for high-sugar and high-fat foods; these data were collected during subjects’ normal daily activities. A measure of dietary self-control in real-world contexts was created by quantifying the degree to which self-reported craving for high-sugar and high-fat foods predicted subsequent consumption of such foods. Study condition (treatment vs. control) predicted changes over time in targeted types of neural activity, which in turn predicted changes over time in real-world dietary self-control performance. This randomized, controlled, single-blind study is the first research we are aware of in which real-world self-control was improved by up-regulating activity in putative self-control regions of the brain.