This paper presents a novel oversampling technique addressing highly imbalanced distributions in benchmark and electroencephalogram (EEG) datasets. Presently, conventional machine learning technologies do not adequately address imbalanced data with an anomalous class distribution and underrepresented data. To balance the class distributions, an extended adaptive subspace self-organizing map (EASSOM) that combines a local mapping scheme and the globally competitive rule is proposed to artificially generate synthetic samples that focus on minority class samples and its application in EEG. The EASSOM is configured with feature-invariant characteristics, including translation, scaling, and rotation, and it retains the independence of the basis vectors in each module. Specifically, basis vectors that are generated via each EASSOM module can avoid generating repeated representative features that only increase the computational load. Several benchmark experimental results demonstrate that the proposed EASSOM method incorporating a supervised learning approach could be superior to other existing oversampling techniques, and three EEG applications present the improvement of classification accuracy using the proposed EASSOM method.