In the domain of electroencephalogram (EEG) research, the generalization of workload classification across different tasks, subjects, and channel configurations remains a significant challenge, primarily due to the field’s tradition of conducting within-task studies in controlled, lab-based environments. This study pioneers the application of domain adaptation techniques to EEG data, aiming to transcend these limitations by facilitating consistent cross-task classification of workload levels. Central to our methodology is the integration of source localization techniques that render EEG data channel agnostic, thus enhancing our model’s capacity to generalize across diverse channel configurations. By utilizing two disparate datasets–one derived from arithmetic tasks and the other from working memory (n-back) tasks—we implement binary classification to discern between low and high workload states. The core of our approach is a modified Convolutional Neural Network (CNN) model, EEGNet, which is specifically designed to capture the temporal and spatial dynamics inherent in EEG data. Enhanced with Maximum Classifier Discrepancy (MCD) for domain adaptation, and bolstered by source localization, this strategic combination enables an impressive 81.76% accuracy in cross-task classification. The success of our strategy in cross-task, cross-channel, and cross-subject classification not only demonstrates its potential for enhancing the generalizability of EEG data analysis but also marks a significant step forward in applying EEG-based workload classification in real-world scenarios, beyond the confines of laboratory settings. The implications of this research are vast, offering a promising avenue for the generalization of EEG data classification across various domains.