The safe operation of a nuclear power plant (NPP) can be guaranteed through the team effort of operators in the main control room (MCR). Among the various features, peer checks, concurrent verification, independent verification, and communication reconfirmation are major contributors to effective operations in the MCR. In the digital MCR environment of advanced NPPs, there are potential emerging issues of concern related to these contributors resulting from the use of PC-soft controls for reactor operations. The objective of this study is to investigate the development of quantitative indicators for estimating the implicit intentions of reactor operators as a way to mitigate such concerns. The proposed quantitative indicators support peer checks and concurrent/independent verifications for diagnosing and preventing human errors through communication enhancement in a digital technology-based MCR. A machine learning-based algorithm was used to classify two implicit intentions of agreement and disagreement. The classification was based on electroencephalography data measured from human subjects while they performed mock operational tasks using soft controls. The mock operational tasks were based on using a Windows-based nuclear plant performance analyzer (Win-NPA). Statistical analysis was performed on the measured data to identify significant differences between the agreement and disagreement judgments by the operators. An average classification accuracy of 72% was achieved by using a support vector machine classifier for the Win-NPA task with a low number of features across the various Brodmann areas. The methodology proposed in this study may also serve to enhance communications in conventional MCRs for human error minimization.