What is it about?
Electroencephalography (EEG) records the brain’s electrical activity and is widely used to help assess conditions such as seizures, brain injury, disorders of consciousness, and coma. In recent years, deep learning has been increasingly used to help computers identify patterns in EEG recordings that may support diagnosis, monitoring, and prognosis. This systematic review examined 37 studies published between 2018 and 2023 to understand how deep learning has been applied to EEG in these clinical settings. The review found encouraging progress, especially in classification tasks, but also several important limitations. Many studies used small datasets, data were often not publicly available, and methods for labeling patients and evaluating models varied considerably. Most research relied on a limited range of deep learning approaches, while techniques that combine different types of information or explain why a model reaches a decision remain underused. The findings highlight the need for larger and better-documented datasets, more consistent evaluation methods, and more explainable models before these technologies can be used reliably across different patients and clinical settings.
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Why is it important?
What makes this work unique is its focused and systematic examination of how deep learning is being used with EEG specifically for assessing brain function and injury, including consciousness disorders, seizures, traumatic brain injury, and coma. Rather than reviewing model performance alone, it considers the full research pipeline—from datasets and preprocessing to model design, validation, hyperparameter optimization, and explainability. The work is also timely because artificial intelligence is increasingly being explored for clinical decision support, while important barriers remain before these methods can be used reliably in practice. The review highlights persistent problems such as small and poorly documented datasets, inconsistent labeling, limited use of multimodal approaches, and insufficient model explainability. By bringing these issues together and identifying clear directions for future research, the article can help researchers, clinicians, and developers better understand both the promise and current limitations of AI-assisted EEG analysis.
Perspectives
From my perspective, this publication represents more than a synthesis of existing research. It highlights how much progress has been made in applying deep learning to EEG analysis, while also showing how far the field still needs to go before these methods can be used confidently in real clinical settings. The review makes clear that strong predictive results alone are not enough: dataset quality, validation, reproducibility, and the ability to understand why a model makes a particular decision are equally important. What I find especially meaningful is the potential for these technologies to support the assessment of patients with serious neurological conditions, including disorders of consciousness, brain injury, seizures, and coma. At the same time, the evidence reviewed in this work encourages caution and methodological rigor. I hope this article can help researchers look beyond accuracy figures and develop approaches that are not only technically effective, but also transparent, clinically meaningful, and ultimately useful to healthcare professionals and patients.
Tiago Silva
Universidade Federal de Uberlandia
Read the Original
This page is a summary of: Deep Learning of EEG Signals for Brain Function and Injury: A Systematic Review, ACM Computing Surveys, September 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3847656.
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