Over the last 30 years we have observed dramatic declines in mental health worldwide, with nearly 450 million people currently suffering from a mental or behavioral disorder. Globally, there is less than 1 mental health professional for every 10,000 people, with 76-85% of the population in low and middle-income countries without access to treatment. The overarching aim of this thesis is the identification of novel and cost-effective methods for measuring, detecting, and assessing well-being. In the first study of this research project, we validated the ability of a quick global scale to capture multidimensional well-being on 1,615 participants that participated in an online survey, identified some predictors of well-being, and observed improvements from online interventions. Mental health and individual well-being also influences the structure and function of our brains across the lifespan, which in turn, mediate well-being levels. While progress has been made regarding our understanding of the interacting relationships between well-being and brain function, much is still unknown. Recent technological advances have led to the development of affordable, light-weight, wearable, and wireless electroencephalography (EEG) technologies that offer fast preparation time, high mobility, and that facilitate the collection of EEG data over large and diversified populations by increasing access to populations that were previously difficult to study with conventional systems. The analysis of large datasets with robust statistical methods or advanced machine-learning algorithms can ease the identification of trends, the mediator role of covariables, and the classification of mental states. While low-cost, low-density EEG systems have presented significant challenges for conducting EEG research, here we validated a wearable system for recording spectral measures relevant to the study of well-being, by comparison with a state-of-the-art system (study 2). In study 3, we used the tools validated in studies 1 and 2 to examine the relation between EEG and multidimensional well-being in a large sample (N = 353). We found a potential EEG marker of well-being, consistent with some literature on anxiety and depression, with age as a mediator. We discuss interpretations and limitations related to the studies and the broader field, as well as future directions (e.g., real-world EEG monitoring, dyadic or multimodal applications, brain-computer interfaces, neurofeedback training) and ethical implications for the field. The broader applications of this line of research will hopefully help to reduce the prevalence of mental health disparities worldwide (e.g., chronic stress, anxiety disorder, depression, psychiatric conditions), and will also help to predict and prevent mental illness in the broader population.