The diagnosis of depression is a critical topic in the medical field. For years, the electroencephalogram (EEG) has been considered an objective and cost-effective detection tool. However, most studies on depression recognition models tend to extract information solely from the original temporal scale of EEG signals, ignoring the usage of coarse scales. This study aims to explore the feasibility of multiscale analysis for a depression recognition model and to research its temporal characteristics. Based on two types of multiscale entropy, this paper constructs a machine learning model using classifiers including LDA, LR, RBF-SVM, and KNN. The relation between the temporal scale and model performance was examined through mathematical analysis. The experimental results showed that the highest classification accuracy achieved was 96.42% with KNN at scale 3. Among various classifiers, scales 3 and 9 outperformed other scales. The model performance is correlated with the scale variation. Within a finite range, an optimal scale likely exists. The algorithm complexity is linearly related to the temporal scale. By accepting predictable computational costs, a stable improvement in model performance can be achieved. This multiscale analysis is practical in building and optimizing a depression recognition model. Further investigation of the relation between the temporal scale and model capabilities could advance the application of computer-assisted diagnosis.

Resting-state electroencephalography (EEG) is a promising noninvasive signal source for depression detection, but its effective use is challenged by spatial mixing across channels, multiscale non-stationarity, and sparsely distributed discriminative evidence. Extracting stable and task-relevant features from complex EEG signals therefore remains a central problem in EEG-assisted depression screening. To address these challenges, this study proposes MSST-TCN, an end-to-end model that integrates Spatial Adaptive Filtering (SAF), Multi-Scale Dilated Convolution (MSDC), and Temporal Attention Pooling (TAP). SAF performs task-driven spatial projection on multichannel EEG to enhance discriminative spatial representations and reduce channel redundancy. MSDC then progressively expands the temporal receptive field using dilated convolutions, enabling hierarchical modeling of short-, medium-, and long-range temporal dependencies. Finally, TAP adaptively aggregates informative temporal segments and alleviates the information dilution caused by global average pooling. The proposed model was evaluated on the MODMA resting-state EEG dataset, and its effectiveness was further examined using t-SNE visualization, spatial-filter decomposition, channel-importance topography, temporal-attention analysis, confusion matrices, and ablation experiments. Under a subject-independent evaluation setting, MSST-TCN achieved an accuracy of 98.54% on the MODMA dataset. These results suggest that jointly optimizing spatial representation, multiscale temporal modeling, and temporal-evidence aggregation can improve the performance and stability of resting-state EEG-based depression detection, while providing a feasible modeling framework for EEG-assisted screening.

Recent studies on EEG-based automated depression detection have primarily depended on complex deep learning models. While these methods improve classification performance, their practical application is limited by high computational complexity, challenging training processes, and poor interpretability. This paper proposes an efficient method for depression recognition, which extracts multi-domain features from preprocessed EEG signals and selects the most discriminative feature subset by integrating the rapid preliminary screening capability of RankSearch with the interactive optimization ability of the Genetic Algorithm (GA). Our approach first eliminates redundant features efficiently through RankSearch, then deeply explores inter-feature relationships via GA, significantly enhancing classification performance while maintaining feature-level interpretability. Using the optimized feature subset, we evaluate performance with multiple machine learning classifiers (Decision Tree, KNN, Random Forest, SVM, XGBoost). Experiments on the public HUSM dataset demonstrate superior performance under rigorous cross-validation (accuracy = 95.08%, sensitivity = 95.99%, specificity = 94.30%, F1-score = 95%, AUC = 0.9514), with feature importance analysis further confirming interpretability. Compared to existing models, our method achieves lower computational complexity and higher clinical practicality, offering a more efficient technical solution for objective depression diagnosis.

The early diagnosis of depression is often impeded by the subjectivity inherent in traditional clinical assessments. To advance objective screening, this study proposes a lightweight neural network framework designed to discriminate between pathological depressive states and non-pathological transient negative emotions using EEG signals. Diverging from conventional methods that rely on single-domain features, we construct a comprehensive multi-domain feature space via Wavelet Packet Decomposition. Specifically, the framework integrates frequency (α/β power spectral density ratio), spatial (normalized α-asymmetry), and non-linear (Sample Entropy) attributes to capture the heterogeneous neurophysiological dynamics of depression. To effectively synthesize these diverse features, a multi-head additive attention mechanism is introduced. This mechanism empowers the model to adaptively recalibrate feature weights, thereby prioritizing the most discriminative patterns associated with the disorder. Experimental validation on the DEAP (negative emotion) and HUSM (major depressive disorder) datasets demonstrates that the proposed method achieves a classification accuracy of 92.2% and an F1-score of 93%. Comparative results indicate that our model significantly outperforms baseline SVM and standard deep learning approaches. Furthermore, the architecture exhibits high computational efficiency and rapid convergence, highlighting its potential as a deployable engine for real-time mental health monitoring in clinical scenarios.