Spectrograms are pivotal in time-frequency signal analysis, widely used in audio processing and computational neuroscience. Chirp-like patterns in electroencephalogram (EEG) spectrograms (marked by linear or exponential frequency sweep) are key biomarkers for seizure dynamics, but automated tools for their detection, localization, and feature extraction are lacking. This study bridges this gap by fine-tuning a Vision Transformer (ViT) model on synthetic spectrograms, augmented with Low-Rank Adaptation (LoRA) to boost adaptability. We generated 100000 synthetic spectrograms with chirp parameters, creating the first large-scale benchmark for chirp localization. These spectrograms mimic neural chirps using linear or exponential frequency sweep, Gaussian noise, and smoothing. A ViT model, adapted for regression, predicted chirp parameters. LoRA fine-tuned the attention layers, enabling efficient updates to the pre-trained backbone. Training used MSE loss and the AdamW optimizer, with a learning rate scheduler and early stopping to curb overfitting. Only three features were targeted: Chirp Start Time (Onset Time), Chirp Start Frequency (Onset Frequency), and Chirp End Frequency (Offset Frequency). Performance was evaluated via Pearson correlation between predicted and actual labels. Results showed strong alignment: 0.9841 correlation for chirp start time, with stable inference times (137 to 140s) and minimal bias in error distributions. This approach offers a tool for chirp analysis in EEG time-frequency representation, filling a critical methodological void.

翻译：谱图在时频信号分析中至关重要，广泛应用于音频处理和计算神经科学领域。脑电图（EEG）谱图中的类啁啾模式（以线性或指数频率扫描为特征）是癫痫发作动力学的关键生物标志物，但目前缺乏用于其检测、定位和特征提取的自动化工具。本研究通过合成谱图微调Vision Transformer（ViT）模型，并结合低秩自适应（LoRA）增强适应性，填补了这一空白。我们生成了包含啁啾参数的100000个合成谱图，创建了首个大规模啁啾定位基准数据集。这些谱图通过线性或指数频率扫描、高斯噪声和平滑处理模拟神经啁啾信号。采用适应回归任务的ViT模型预测啁啾参数，通过LoRA对注意力层进行微调，实现对预训练主干网络的高效更新。训练使用均方误差损失和AdamW优化器，配合学习率调度器和早停策略以抑制过拟合。模型仅针对三个特征进行预测：啁啾起始时间、啁啾起始频率和啁啾终止频率。通过预测标签与真实标签的皮尔逊相关性评估性能，结果显示：啁啾起始时间相关性达0.9841，推理时间稳定（137至140秒），误差分布偏差极小。该方法为EEG时频表征中的啁啾分析提供了有效工具，填补了重要的方法论空白。