<ul><li>Researchers have developed a fine-tuned Transformer model to detect and localize chirp-like patterns in EEG spectrograms, which are important biomarkers for seizure dynamics.</li><li>The study utilized synthetic spectrograms with chirp parameters to create a benchmark for chirp localization.</li><li>The Vision Transformer (ViT) model was adapted for regression to predict chirp parameters, and attention layers were fine-tuned using Low-Rank Adaptation (LoRA).</li><li>The model achieved a strong alignment between predicted and actual labels, with a correlation of 0.9841 for chirp start time.</li></ul>

Chirp Localization via Fine-Tuned Transformer Model: A Proof-of-Concept Study

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