The review, conducted by Atefeh Abedzadeh Attar, Mohammad Hossein Moattar and Esmaeil Kheirkhah of Islamic Azad University in Mashhad, Iran, systematically analyzed 69 studies published between 2020 and 2026 that apply machine learning (ML) and deep learning (DL) techniques to electroencephalography (EEG) and event-related potentials (ERPs) for depression diagnosis. The authors searched Scopus and Web of Science, screened 187 initial records down to the final set, and organized the field around a complete analytical pipeline: signal preprocessing, feature engineering, feature selection, and classification. Their central message is that artificial intelligence can extract objective, reproducible biomarkers of depression from brain signals that clinicians currently cannot see with the naked eye.

Why EEG? Unlike MRI and fMRI, which are expensive, immobile and offer poor temporal resolution, EEG is cheap, portable and captures neural activity at millisecond precision. It records five canonical frequency bands—delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–40 Hz)—each tied to different cognitive states. ERPs, the brain’s stereotyped voltage responses to specific stimuli, add another layer: depressed patients show characteristic abnormalities in components such as P1 and P2 and heightened sensitivity to negative stimuli. Together, resting-state EEG and stimulus-evoked ERPs offer a complementary window into the neural signatures of the disorder, something the review emphasizes is often missed by studies that examine only resting-state data.

Before any algorithm can learn, the raw signals must be cleaned. EEG recordings are contaminated by eye movements, muscle activity and power-line interference, so the reviewed studies relied heavily on finite impulse response filters, discrete wavelet transforms, independent component analysis, notch filters and even Kalman filters to strip out noise. A second, less obvious problem is dimensional explosion: a single recording can yield thousands of features from only dozens of patients, inviting overfitting. The authors document how researchers tamed this with principal component analysis, locally linear embedding, correlation and variance thresholds, and in one extreme case compressed a set of 10,800 features before modeling even began.

Feature engineering emerges as the heart of the enterprise, and the review organizes it into three families. Handcrafted approaches extract interpretable quantities directly: power spectral density across frequency bands, synchronization measures such as the self-synchronization index and the phase lag index, functional connectivity matrices, and nonlinear descriptors like Lempel–Ziv complexity and various entropies. End-to-end deep learning models—convolutional neural networks, CNN-LSTM hybrids, attention mechanisms, graph convolutional networks and even spiking neural networks—learn their own representations from raw or transformed signals. Between the two sit hybrid methods, which feed manually engineered features such as STFT spectrograms or directed connectivity measures into deep networks, combining human domain knowledge with automated pattern discovery.

The performance numbers are striking. On the machine learning side, accuracies ranged from 82.68 to 100 percent, with support vector machines and K-nearest neighbors dominating the literature; boosting methods such as XGBoost reached 98.92 percent, and one lightweight LightGBM framework hit 97.42 percent using only three prefrontal electrodes—evidence that wearable-scale screening is technically feasible. Deep learning models ranged from 77.78 to 100 percent, with CNN-LSTM architectures reaching 99.9 percent in a real-time wearable system called DepCap, and attention-based and graph-based models consistently exceeding 95 percent. Reported biomarkers converge on frontal and temporal regions, elevated theta and alpha power, interhemispheric asymmetry in delta, alpha and beta bands, and disrupted functional connectivity in parietal-occipital networks.

But the review delivers a sharp caution about those headline numbers. Several studies reporting perfect 100 percent accuracy relied on validation schemes that may leak subject-specific information: when EEG recordings are chopped into segments and randomly split into training and test sets, segments from the same person can appear on both sides, letting the model memorize individuals rather than learn the disorder. One study’s accuracy fell from 100 percent under ordinary 10-fold cross-validation to 83.96 percent under leave-one-subject-out cross-validation, a dramatic illustration of the problem. The authors argue that subject-independent validation, transparent data partitioning and strict leakage controls should be mandatory before any accuracy claim is taken at face value.

The two AI paradigms also carry distinct trade-offs. Machine learning models are interpretable and computationally cheap, letting clinicians trace which biomarkers drove a decision, and they perform well on small datasets—but they depend on laborious, error-prone manual feature pipelines. Deep learning models are robust to noise and capable of real-time monitoring, early detection and severity tracking, yet they demand large datasets, heavy computation, and suffer from black-box opacity that undermines clinical trust. The review also flags ethical concerns: most studies enrolled fewer than 100 participants, raising doubts about generalizability across populations, and over-reliance on automated systems risks eroding the human empathy central to mental health care. The authors call for explainable AI techniques, adherence to reporting standards such as TRIPOD and PROBAST, and patient-centered deployment.

Looking forward, the review sketches a roadmap for the field: larger and more diverse public datasets with standardized acquisition protocols, multimodal models that fuse EEG with speech, eye-tracking and clinical records, federated learning to share knowledge across hospitals without moving sensitive patient data, and foundation models pre-trained on large biomedical corpora to overcome the small-data bottleneck. It also highlights a practical dual trend—increasingly sophisticated architectures on one hand, and radically simplified systems using as few as two frontal electrodes on the other—that could bring objective, AI-assisted depression screening out of the laboratory and into clinics, wearables and underserved communities worldwide. If those validation and equity challenges are met, the authors conclude, brain-signal-based AI could transform depression diagnosis from a subjective art into an objective, scalable science.

Subject of Research: Machine learning and deep learning approaches for diagnosing depressive disorders from EEG and ERP signals

Article Title: A review on machine learning and deep learning approaches for depressive disorder diagnosis based on EEG and ERP signals

Article References: Abedzadeh Attar, A., Moattar, M. H., & Kheirkhah, E. (2026). A review on machine learning and deep learning approaches for depressive disorder diagnosis based on EEG and ERP signals. Discover Artificial Intelligence, 6(1), Article 1308. https://doi.org/10.1007/s44163-026-02334-5

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02334-5

Keywords: depression, EEG, ERP, machine learning, deep learning, brain signals, diagnosis, biomarkers, neural networks, mental health, feature engineering, clinical AI