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This study investigated whether five frontal dry-electrode electroencephalography (EEG) channels can support recognition of mental fatigue-related states after individual calibration. Thirteen healthy adult men completed pre- and post-rest recordings, N-back, Stroop, and a 15-min sustained attention to response task (SART). EEG was recorded from FP1, FP2, AF7, FPz, and AF8. Following quality control, 12 participants were retained for EEG analyses. Spectral, Hjorth, entropy, asymmetry, and inter-channel correlation features were extracted from the early and late SART periods. A subject-dependent nested contiguous temporal-block cross-validation framework selected temporal aggregation, feature set, classifier, and feature number using training data only. KSS increased from 1.77±0.73 to 5.15±0.55 after SART (p < 0.001). No-go commission errors increased from 25.44%±16.63% to 35.87% ± 20.85% (p = 0.040), whereas Go median reaction time decreased (p < 0.001). The main model achieved a mean balanced accuracy (BA) of 0.831±0.124, a median BA of 0.850, and a mean area under the receiver-operating-characteristic curve (AUC) of 0.920±0.114. Chronological holdout testing yielded a mean BA of 0.774±0.204, whereas strict cross-subject classification remained near chance. These findings support the use of five frontal dry-electrode EEG channels for subject-dependent monitoring of mental fatigue-related states after individual calibration, but do not yet support general classification without target-subject calibration.