Program comprehension is one of the important cognitive processes in software maintenance. The process typically involves diverse mental activities such as understanding of source code, library usages, and requirements. Systematic supports would be improved if the supports can be aware of such fine-grained mental activities during program comprehension. Here we aim to investigate whether biometric data can be varied according to such mental activity classes and conduct an experiment with program comprehension tasks involving multiple documents. As a result, we successfully classified the success/failure of the tasks at 85.2% from electroencephalogram (EEG) combined with focused document types. This result suggests that our metrics based on EEG and focused document types might be beneficial to detect developers’ diverse mental activities triggered by different documents.