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

The study, authored by Mengwen Xiao, Kangrui Du and Ziqi Lin, sits at the intersection of two research traditions that have matured rapidly over the past decade. The first is representational alignment, a family of techniques that quantify the similarity between the internal states of artificial neural networks and the activity patterns recorded from living brains. The second is chain-of-thought reasoning, the strategy by which large language models decompose complex problems into intermediate steps before committing to an answer. The new research connects these traditions in a striking way: instead of using brain activity only as a post-hoc yardstick, the authors use it as a training signal, encouraging a model’s internal representations to track those observed in human neural data while it reasons.

To appreciate why this matters, it helps to recall how the relationship between brains and machines has typically been studied. In a landmark 2023 analysis, researchers including Alex Zador and colleagues argued in Nature Communications that understanding natural intelligence requires studying both brains and their artificial analogues together, a perspective that helped galvanize the NeuroAI community. Subsequent work by Greta Tuckute and collaborators, published in Nature Human Behaviour in 2024, showed that language models vary widely in how well their internal states predict neural responses to sentences, and that this predictive power relates to how ‘brain-like’ a model’s representations are. Until recently, however, such alignment scores were largely descriptive. They told researchers which models resembled the brain, but they did not tell the models how to become more brain-like, nor whether doing so would improve them.

The conceptual shift from passive alignment to active neural guidance is the heart of the new work. In the passive paradigm, a trained model is frozen and its hidden activations are compared, layer by layer, with recordings such as functional MRI or magnetoencephalography taken while people read or solve problems. In the active paradigm described by Xiao and colleagues, the comparison happens during learning. The model receives an additional objective that rewards representational similarity to human neural data, effectively telling the network, at each stage of processing, that its internal geometry should not drift too far from the geometry observed in the brain. This turns the brain from a mirror into a compass, and the consequences for reasoning performance are substantial.

The technical machinery behind this idea draws on a rich literature. Representational similarity analysis and related encoding models provide differentiable measures of alignment, meaning that the gap between a model’s internal states and recorded brain activity can be computed as a quantity that gradient descent can minimize. Changde Du and Huiguang He, writing in an accompanying News and Views article in Nature Machine Intelligence, place the new study in the context of their own earlier work, published in the same journal in 2025, which explored how neural signals can inform machine learning systems. The new contribution extends this program from perception and language understanding into the domain of multi-step reasoning, where errors compound across steps and small representational deviations can cascade into dramatically wrong conclusions.

Reasoning is precisely where large language models remain most fragile. Chain-of-thought prompting, introduced by Jason Wei and colleagues at NeurIPS in 2022, showed that asking models to generate intermediate reasoning steps can unlock dramatic gains on arithmetic, logic and symbolic tasks. Yet subsequent analyses, including a large-scale study by Abulhair Saparov and colleagues in 2023, revealed that models often succeed on familiar reasoning patterns while failing catastrophically on novel ones, suggesting that their apparent reasoning may rest on shallow pattern matching rather than robust inference. If human neural representations encode something about the structure of valid inference that current training objectives fail to capture, then aligning with those representations during training could inject exactly the inductive bias that chain-of-thought systems lack.

The evidence reviewed by Du and He suggests that this is more than speculation. When the neural-guided objective is incorporated into training, the resulting models not only show stronger correspondence with human brain activity but also demonstrate improved reliability on reasoning benchmarks, producing more consistent and accurate chains of intermediate steps. The authors of the News and Views piece frame this as a move ‘from passive alignment to active neural guidance,’ a phrase that captures the dual payoff of the approach. Alignment ceases to be a descriptive statistic and becomes a functional ingredient of learning, and the improvement in reasoning suggests that the brain’s representational structure carries information that is genuinely useful for artificial inference, not merely an incidental byproduct of shared training data.

The finding also speaks to a long-running debate about what language models actually learn. Ev Fedorenko, Steven Piantadosi and Edward Gibson argued in Nature in 2024 that formal linguistic competence, the ability to produce fluent grammatical language, can be dissociated from functional linguistic competence, the ability to use language for thinking. Large models have largely conquered the former while remaining uneven at the latter. Neural guidance offers a possible bridge: if the internal representations that support human functional competence can be transferred, even partially, into artificial systems, then machines might acquire not just the form of reasoning but something closer to its substance. Related work by Juan Prado, Annapurni Chadha and James Booth, published in the Journal of Cognitive Neuroscience in 2011, mapped the neural substrates of arithmetic processing in children, illustrating the kind of structured neural data that such alignment approaches can exploit.

Independent work reinforces the momentum behind this direction. A 2026 study by Jie Chen, Yue Qi, Yu Wang and Guang Pan in Nature Communications reported further evidence that neural signals can inform and improve machine learning systems, indicating that the strategy is not an isolated result but part of an emerging research program. Together, these studies suggest that the coming years will see increasingly tight coupling between neuroimaging pipelines and model training loops, with brain data flowing not only into evaluation suites but into the loss functions themselves. The practical challenges remain considerable: collecting high-quality neural recordings at scale is expensive, individual differences in brain organization complicate generalization, and the causal question of why neural alignment helps reasoning is only beginning to be answered.

Still, the implications are hard to overstate. If the human brain can serve as a reliable guide for training artificial reasoners, then neuroscience and machine learning are no longer parallel enterprises exchanging occasional insights but a single, integrated discipline in which each field sharpens the other. Models guided by neural signals could become more trustworthy partners in science, medicine and education, while the act of aligning machines to minds may itself reveal which aspects of neural computation are essential to intelligence and which are biological accident. The work of Xiao, Du and Lin, and the perspective offered by Du and He, mark an early but compelling step in that direction, transforming the humble brain scan from a passive record of thought into an active instrument for teaching machines how to think.

Subject of Research: Using human brain signals to actively guide and improve the reasoning of artificial intelligence models

Article Title: Steering machine reasoning with brain signals

Article References: Du, C., & He, H. (2026). Steering machine reasoning with brain signals. Nature Machine Intelligence. https://doi.org/10.1038/s42256-026-01302-z

Image Credits: AI Generated

DOI: 10.1038/s42256-026-01302-z

Keywords: NeuroAI, representational alignment, large language models, chain-of-thought reasoning, brain signals, machine learning, neural encoding, Nature Machine Intelligence, cognitive neuroscience, artificial reasoning, fMRI, brain-inspired AI