<p>Sleep is beneficial to the restoration of the mind and general brain health. Multi-channel electroencephalography (EEG) systems are the traditional method of sleep analysis, but are costly, technologically demanding, and impractical for real-world use. This paper presents an end-to-end classification framework using self-recorded single-channel wearable EEG data collected under real-world resting conditions, capturing natural inter-session variability and noise characteristics of practical wearable acquisition. Eight spectral power features were extracted across Delta, Theta, Alpha1, Alpha2, Beta1, Beta2, Gamma1, and Gamma2 bands. Four classifiers Support Vector Machine, Random Forest, Multi-Layer Perceptron and XGBoost were evaluated under both multivariate and univariate band-wise settings. To ensure reproducibility, 5-fold stratified cross-validation was applied across all models using leakage-free pipelines. XGBoost and Random Forest achieved the highest CV accuracy of 82.49Dataset will be made available on reques% (± 1.75%) and 81.85% (± 1.59%) respectively, with ROC-AUC of 0.891 for both ensemble models. Univariate analysis revealed that gamma-band activity alone achieves 81.5% accuracy, identifying it as the dominant spectral indicator of post-sleep cognitive recovery. These results demonstrate the feasibility of low-cost single-channel EEG for practical sleep-state classification.</p>