Abstract
This chapter examines the use of Generative Adversarial Networks (GANs) in analyzing electroencephalogram (EEG) data. EEG is an electrophysiological method that records brain activity. EEG is used to diagnose neurological disorders and is also very important for brain-computer interface (BCI) systems. Although EEG data processing and analysis is widely used, it faces some difficulties, which reveals the necessity of advanced signal processing techniques. GANs, on the other hand, are advanced machine learning techniques and play an essential role in EEG data analysis. GANs are known for their ability to produce synthetic data similar to actual data, and this feature provides significant advantages in the analysis of EEG data. In particular, GANs are effective at filtering noise, improving data quality, and generating synthetic data. Given the complexity and diversity of EEG data, caution must be exercised in training GAN models and the accuracy of synthetic data. Current limitations of GANs in EEG data analysis and ongoing research to overcome these limitations are also examined.
Published Version
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