Brain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks
Bridging the gap between neural activity and generative models to decode visual perception from brain waves.
Reconstructing visual stimuli from electroencephalography (EEG) is difficult because scalp measurements have high temporal but limited spatial resolution, and paired EEG-image datasets remain small relative to modern generative-model training corpora. We present a reproducible si...
ArXiv CS.CV
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This research marks a significant step toward non-invasive brain-computer interfaces by bridging the gap between low-resolution neural signals and high-fidelity visual reconstruction. Overcoming the limitations of sparse EEG data opens new pathways for understanding how human perception can be digitally decoded in real-time.
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