Decoding of the speech envelope from EEG using the VLAAI deep neural network.

Accou, B.; Vanthornhout, J.; Van hamme, H.; Francart, T.

doi:10.1101/2022.09.28.509945

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bioRxiv · 10.1101/2022.09.28.509945

Decoding of the speech envelope from EEG using the VLAAI deep neural network.

Accou, B.·Vanthornhout, J.·Van hamme, H.·Francart, T.

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Abstract

To investigate the processing of speech in the brain, commonly simple linear models are used to establish a relationship between brain signals and speech features. However, these linear models are ill-equipped to model a highly-dynamic, complex non-linear system like the brain, and they often require a substantial amount of subject-specific training data. This work introduces a novel speech decoder architecture: the Very Large Augmented Auditory Inference (VLAAI) network. The VLAAI network outperformed state-of-the-art subject-independent models (median Pearson correlation of 0.19, p < 0.001), yielding an increase over the well-established linear model by 52%. Using ablation techniques we identified the relative importance of each part of the VLAAI network and found that the non-linear components and output context module influenced model performance the most (10% relative performance increase). Subsequently, the VLAAI network was evaluated on a holdout dataset of 26 subjects and publicly available unseen dataset to test generalization for unseen subjects and stimuli. No significant difference was found between the holdout subjects and the default test set, and only a small difference between the default test set and the public dataset was found. Compared to the baseline models, the VLAAI network still significantly outperformed all baseline models on the public dataset. We evaluated the effect of training set size by training the VLAAI network on data from 1 up to 80 subjects and evaluated on 26 holdout subjects, revealing a logarithmic relationship between the number of subjects in the training set and the performance on unseen subjects. Finally, the subject-independent VLAAI network was fine-tuned for 26 holdout subjects to obtain subject-specific VLAAI models. With 5 minutes of data or more, a significant performance improvement was found, up to 34% (from 0.18 to 0.25 median Pearson correlation) with regards to the subject-independent VLAAI network.

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Accou, B., Vanthornhout, J., Van hamme, H., Francart, T.. 2022-09-30. Decoding of the speech envelope from EEG using the VLAAI deep neural network.. https://doi.org/10.1101/2022.09.28.509945

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