The Greedy Localization Technique (GLT) presents a novel signal processing approach for Brain-Computer Interface (BCI) applications that integrate data-derived and model-driven framework features. Mobile cloud computing (MCC) is mostly preferred to meet field environment conditions which require handling data processing with added attention to energy efficiency and real-time capability demand. Our research introduces a cloud-based BCI framework that addresses these obstacles through an adaptive channel selection process and provides resource optimization features. The method entails source localization using combined features and feature selection of EEG data to create a robust system to optimise real-time BCI channel selection processes. The research aims to focus on designing adaptable EEG channel selection methodology and offload decision capabilities along with enhancing classification precision using reduced resources and data transmission. The proposed methodology is compared with state-of-the-art dimensional reduction methods like recursive feature elimination (RFE), principal component analysis (PCA) and Pearson’s coefficient correlation (PCC). The results signify proposed methodology justifies the real-time signal analysis in various network environments. Our system achieves increased efficiency in BCI data processing with high accuracy maintenance using a combined approach that synthesizes global and local data understanding thus benefitting real-time BCI applications.