A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond
Read the original on arXiv AI →This survey reviews recent advances in converting non‑invasive EEG signals into images, text, and audio using generative AI techniques such as GANs, VAEs, transformers, and diffusion models. It summarizes datasets, feature‑encoding methods, evaluation metrics, and key challenges, noting that EEG‑to‑image models mainly use encoder‑decoder architectures, EEG‑to‑text leverages transformer language models, and EEG‑to‑audio maps signals to mel‑spectrograms for vocoder synthesis. The paper highlights the limitations of small, heterogeneous datasets, poor cross‑subject generalization, and the lack of standardized benchmarks, while providing open‑source resources to support reproducible research.
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