An LMMSE-based Estimation of Temporal Response Function in Auditory Attention Decoding

Kuruvila I, Fischer E, Hoppe U (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2020-July

Pages Range: 2837-2840

Conference Proceedings Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Event location: Montreal, QC CA

ISBN: 9781728119908

DOI: 10.1109/EMBC44109.2020.9175866

Abstract

One of the remarkable abilities of humans is to focus the attention on a certain speaker in a multi-speaker environment that is known as the cocktail party effect. How the human brain solves this non-trivial task is a challenge that the scientific community has not yet found answers to. In recent years, progress has been made thanks to the development of system identification method based on least-squares (LS) that maps the variations between the cortical signals of a listener and the speech signals present in an auditory scene. Results from numerous two-speaker experiments simulating the cocktail party effect have shown that the auditory attention could be inferred from electroencephalography (EEG) using the LS method. It has been suggested that these methods have the potential to be integrated into hearing aids for algorithmic control. However, a major challenge remains using LS methods such that a large number of scalp EEG electrodes are required in order to get a reliable estimate of the attention. Here we present a new system identification method based on linear minimum mean squared error (LMMSE) that could estimate the attention with the help of two electrodes: one for the true signal estimation and other for the noise estimation. The algorithm is tested using EEG signals collected from ten subjects and its performance is compared against the state-of-the-art LS algorithm.

Authors with CRIS profile

Ivine Kuruvila Department of Otorhinolaryngology – Head and Neck Surgery Ulrich Hoppe Professur für Audiologie

How to cite

APA:

Kuruvila, I., Fischer, E., & Hoppe, U. (2020). An LMMSE-based Estimation of Temporal Response Function in Auditory Attention Decoding. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 2837-2840). Montreal, QC, CA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Kuruvila, Ivine, Eghart Fischer, and Ulrich Hoppe. "An LMMSE-based Estimation of Temporal Response Function in Auditory Attention Decoding." Proceedings of the 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, QC Institute of Electrical and Electronics Engineers Inc., 2020. 2837-2840.

BibTeX: Download