A Deep Neural Network for Predicting Continuous Human EEG Across the Auditory Pathway in Response to Sound
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a causal deep neural network that maps binaural audio to high-sample-rate EEG, demonstrating the ability to reproduce established auditory physiological phenomena across multiple paradigms. The technical contribution is significant in its integration of diverse stimuli and subjects into a single model, offering a promising direction for in silico experimentation in auditory neuroscience and hearing technology development.
Methodology
The authors propose a "foundation model" for auditory electrophysiology, utilizing a causal encoder-decoder architecture (WaveNet-based) to map binaural acoustic waveforms directly to high-sample-rate EEG. The methodology is robust, employing a 24-band filterbank, log-compression, and a 9-layer causal WaveNet encoder with dilated convolutions. A key technical contribution is the use of a Short-Time Fourier Transform (STFT) domain loss function that penalizes phase and amplitude errors, which is more suitable for EEG signal reconstruction than standard MSE. The inclusion of a parallel linear pathway to model stimulus-locked electromagnetic artifacts is a sophisticated engineering choice that improves training stability. The model is conditioned on subject identity and demographics, allowing for potential personalization, though the primary evaluation uses a "default" subject embedding.
Results & evaluation
The evaluation is comprehensive, testing the model on three distinct paradigms: parallel auditory brainstem responses (pABR), speech temporal response functions (TRFs), and binaural interaction components (BIC). The dataset is substantial (250 hours, 92 subjects), covering diverse stimuli (speech, music, tonebursts) and electrode montages. The results show that the model reproduces established physiological phenomena, such as frequency-dependent ABR morphology and rate-dependent latency/amplitude changes. Statistical comparisons (Crawford-Howell tests) indicate that model predictions fall within the distribution of human subject-level correlations, validating the model's biological plausibility. However, the lack of a direct comparison with state-of-the-art non-foundation models (e.g., specific ABR predictors or TRF estimators) limits the clarity of its relative performance advantage.
Reproducibility
The paper provides detailed architectural specifications (kernel sizes, dilations, filterbank parameters) and training hyperparameters (learning rate, batch size, loss function details). The description of the artifact pathway and the STFT loss is sufficient for replication. However, the code and pre-trained weights are not explicitly linked in the provided text, and the specific implementation of the "default subject" zero-shot mechanism could benefit from further clarification. The data curation process (clock drift correction, resampling) is described, which is crucial for high-sample-rate EEG work.
Limitations
The model is trained on a relatively young cohort (mean age 22.6 years), limiting generalizability to older populations or those with hearing loss. The evaluation relies heavily on correlation with grand averages and published statistics rather than direct prediction accuracy metrics (like SNR or RMSE) against held-out individual subjects in a standardized way. The "foundation model" claim is somewhat strong given the single-modality input (audio) and output (EEG) and the specific domain of auditory physiology; it is more accurately described as a specialized domain-specific model. The paper does not discuss computational cost or real-time inference capabilities, which are critical for the proposed hearing aid applications.
Broader impact
This work has significant potential for advancing computational auditory neuroscience by providing a unified framework for simulating brain responses to sound. It could accelerate the development of hearing technologies by enabling in silico testing of hearing aid algorithms. The causal nature of the model makes it suitable for real-time applications. However, the impact is currently limited to the auditory domain and specific electrophysiological signals, and broader impact would require validation on larger, more diverse datasets and integration with other neural signals. The paper presents a causal deep neural network that maps binaural audio to high-sample-rate EEG, demonstrating the ability to reproduce established auditory physiological phenomena across multiple paradigms. The technical contribution is significant in its integration of diverse stimuli and subjects into a single model, offering a promising direction for in silico experimentation in auditory neuroscience and hearing technology development.
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