Generative adversarial networks in EEG analysis: an overview.
Ahmed G Habashi, Ahmed M Azab, Seif Eldawlatly and 1 others
PMID 37038142WHAT IT FOUND
This is a technical review of algorithms that create fake EEG signals to train computer models.
It contains no patient data or clinical outcomes. It does not change how therapists treat patients.
Key findings
01The paper reviews 43 technical studies on using Generative Adversarial Networks to augment EEG data for tasks like motor imagery and emotion recognition.
02The authors state that current methods for generating these signals have no standard way to measure if the fake data is good enough for clinical use.
STILL TO COME
How it was doneWhat they found
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What it does not show
This is a review of computer science methods, not a clinical trial. No patients were recruited or treated in this study. The outcomes reported are accuracy percentages for software algorithms, not health improvements for people. The authors note that the quality of the generated brain signals is difficult to measure and may not reflect real clinical needs.
Declared interests
The work was funded by Ain Shams University. The authors declared no other conflicts of interest.
The easy way to misread this
Do not interpret the improved classification accuracy of these computer algorithms as evidence that any treatment is effective for patients. This paper describes how to generate fake brain signals for software training, not how to treat clinical conditions.