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In real-world scenarios, quantitative assessment of mental workload level (MWL) is crucial for preventing performance degradation. Although prior research has explored the feasibility of EEG-based MWL assessment, the widely employed training-testing classification approach using multichannel EEG features limits its practical applications. Here, we introduced an analysis framework to explore the feasibility of detecting multiple MWLs by utilizing brain rhythm sequence (BRS) from the optimal single EEG channel. EEG data were collected from 32 participants undergoing a simulated flight task with three MWLs and two interfaces [i.e., computer screen (CS); virtual reality (VR)]. At an individual level, the BRS of each channel was estimated and compared with the template BRS that was obtained from a pre-defined 12-s window at each MWL. The optimal channel was determined individually as that with the highest similarity between the BRSs and the template BRS, whereas the accuracy was estimated as the ratio between the number of correctly identified BRSs and the total number. The proposed framework achieved a satisfactory classification accuracy above 70% from three MWLs (i.e., CS: 71.46% ± 11.83%, VR: 73.07% ± 11.47%) through averaging the performance of subject-dependent optimal channel. By further analysis of the spatiospectral patterns of the BRS, we revealed that delta and alpha bands residing in the frontal, temporal, and central areas played a key role in assessing MWL. Moreover, distinct spatio-spectral patterns were observed between the two interfaces, indicating the underlying complex neural mechanisms. Overall, our results represent a promising avenue for the development of practical lightweight detection systems that may benefit real-world operators whose performance is prone to overload/underload.