Sea-Wave: Speech Envelope Reconstruction From Auditory EEG With an Adapted WaveNet

Abstract

Speech envelope reconstruction from EEG is shown to bear clinical potential to assess speech intelligibility. Linear models are commonly used to this end, but they have recently been outperformed in reconstruction scores by non-linear deep neural networks, particularly by dilated convolutional networks. This study presents Sea-Wave, a WaveNet-based architecture for speech envelope reconstruction that outperforms the state-of-the-art model. Our model is an extension of our submission for the Auditory EEG Challenge of the ICASSP Signal Processing Grand Challenge 2023. We improve upon our prior work by evaluating model components and hyperparameters through an ablation study and hyperparameter search, respectively. Our best subject-independent model achieves a Pearson correlation of 22.58% on seen and 11.58% on unseen subjects. After subject-specific fine-tuning, we find an average relative improvement of 30% for the seen subjects and a Pearson correlation of 56.57% for the best seen subject.Finally, we explore several model visualizations to obtain a better understanding of the model, the differences across subjects and the EEG features that relate to auditory perception.