Mental fatigue (MF) is a common issue that impairs cognitive function and general well-being. Existing electroencephalogram (EEG)-based neurofeedback is time-consuming because it necessitates multiple follow-up sessions. Therefore, this paper proposes a noninvasive and personalized real-time mental fatigue intervention for online learners using BrainComputer Interface (BCI). The model consists of two components: (1) MF detection, and (2) MF intervention. The Emotiv Insight will be used to collect EEG signals during online learning sessions. The mental fatigue detection model will be formulated based on 6 Emotiv’s Performance Metrics (EPM). To intervene, the monitor contrast will be used to reduce mental fatigue. The model will be validated based on Chalder Fatigue Questionnaire (CFQ). Future research can focus on optimizing the intervention technique and testing the effectiveness of the model in different populations.