Depression is a major global health concern, and early detection remains critical for timely intervention. Electroencephalography (EEG) provides a non-invasive means of identifying neurophysiological patterns associated with depressive disorders. However, traditional clinical-grade EEG systems are expensive, require complex setup, and are confined to laboratory environments. In contrast, low-cost consumer-grade EEG headsets—such as Muse, Emotiv, and OpenBCI—offer portability and affordability but are often criticized for limited channel count, lower sampling rates, and higher susceptibility to noise. This study presents a systematic comparative analysis of clinical- and consumer-grade EEG devices for automated depression detection. Using both public datasets and paired recordings, we evaluate signal fidelity, feature discriminability, and classification accuracy across multiple machine-learning and deep-learning models. The proposed evaluation pipeline (Figure 2) includes standardized preprocessing, artifact removal, and feature extraction methods, while Table 1 summarizes device specifications and Table 2 lists the datasets employed. Results demonstrate that, although clinical systems outperform consumer devices in signal quality and peak accuracy, optimized preprocessing and transfer-learning models significantly narrow the gap, yielding only marginal differences in classification outcomes. These findings indicate that consumer-grade EEG can serve as a viable alternative for preliminary depression screening, enabling scalable and cost-effective mental-health monitoring in real-world settings.