发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一个因果深度神经网络，将双耳声波直接映射为高采样率EEG，在250小时、92名受试者数据上训练，成功预测ABR、TRF和BIC，验证了单一模型跨范式捕获听觉生理的可行性。
AI 中文摘要
听觉生理学的计算模型通常针对听觉通路的特定反应或阶段，限制了其跨实验范式和神经时间尺度整合发现的能力。我们提出了一个人类听觉电生理学的基础模型：一个因果神经网络，经过训练可将双耳声学波形直接映射到高采样率的脑电图（EEG）。该模型在来自92名受试者的大约250小时EEG数据上进行了训练，数据包含不同的电极布局以及涵盖短纯音、语音和音乐的刺激。我们测试了该模型是否能恢复刺激速率、频率和呈现方式对听觉脑干反应（ABRs）的影响；对连续语音的皮层下和皮层时间响应函数（TRFs）；以及点击诱发的双耳相互作用成分（BIC）。预测的ABRs和TRFs再现了已确立的反应形态和刺激依赖性效应，模型大平均相关性落在相应的受试者水平人类分布范围内。模型预测的BIC指标与文献中报告的值非常接近。这些发现表明，一个单一的音频到EEG模型可以捕获跨范式和时间尺度的听觉生理学，支持未来的计算机模拟实验和听力技术应用。
英文摘要
Computational models of auditory physiology commonly target specific responses or stages of the auditory pathway, limiting their ability to integrate findings across experimental paradigms and neural timescales. We present a foundation model of human auditory electrophysiology: a causal neural network trained to map binaural acoustic waveforms directly to high-sample-rate EEG. The model was trained on approximately 250 hours of EEG data from 92 subjects, with varied electrode montages and stimuli spanning tonebursts, speech, and music. We tested whether the model recovered effects of stimulus rate, frequency, and presentation method on auditory brainstem responses (ABRs); subcortical and cortical temporal response functions (TRFs) to continuous speech; and the click-evoked binaural interaction component (BIC). Predicted ABRs and TRFs reproduced established response morphology and stimulus-dependent effects, with model-grand-average correlations falling within the corresponding subject-level human distributions. The model-predicted BIC metrics closely resembled the values reported in the literature. These findings demonstrate that a single audio-to-EEG model can capture auditory physiology across paradigms and timescales, supporting future in silico experimentation and hearing technology applications.
Comments5 pages. Submitted to ICASSP 2027. v2: Update funding acknowledgment
