Head of division

Prof. Dr. Dr. Birger Kollmeier

+49 (0)441 798 5466 oder 5470

W30 3-313


Katja Warnken

+49 (0)441 798 5470

+49 (0)441 798-3902

W30 3-312

Kirsten Scheel

+49 (0)441 798-3813

+49 (0)441 798-3902

W30 3-312

Address (Mail address)

Medizinische Physik, Fakultät VI
Universität Oldenburg
26111 Oldenburg

Location / How to find us

For specific questions regarding one of our research topics, please contact the respective people directly (see staff list).

Paper 2018 de Taillez Kollmeier Meyer Machine learning

Machine learning for decoding listeners’ attention from EEG evoked by continuous speech

Tobias de Tailléz, Birger Kollmeier and BErnd T. Meyer (2018)
European Journal of Neuroscience, online First 04.12.2017. doi:10.1111/ejn.13790

Previous research has shown that it is possible to predict which speaker is attended in a multi-speaker scene by analyzing a listener's EEG activity. In this study, existing linear models that learn the mapping from neural activity to an attended speech envelope are replaced by a non-linear neural network. The proposed architecture takes into account the temporal context of the estimated envelope, and is evaluated using EEG data obtained from 20 normal-hearing listeners who focused on one speaker in a two-speaker setting. The network is optimized with respect to the frequency range and the temporal segmentation of the EEG input, as well as the cost function used to estimate the model parameters. To identify the salient cues involved in auditory attention, a relevance algorithm is applied that highlights the electrode signals most important for attention decoding. In contrast to linear approaches, the neural network profits from a wider EEG frequency range (1-32 Hz) and achieves a performance seven times higher than the linear baseline. Relevant EEG activations following the speech stimulus after 170 ms at physiologically plausible locations were found. This was not observed when the model was trained on the unattended speaker. Our findings therefore indicate that non-linear neural networks can provide insight into physiological processes by analyzing EEG activity.

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