Riemannian geometry-based decoding of the directional focus of auditory attention using EEG

TL;DR

This paper introduces a Riemannian geometry-based classification method for decoding the directional focus of auditory attention from EEG signals, offering improved performance over existing CSP-based methods for longer decision windows.

Contribution

The paper presents a novel Riemannian geometry-based classification approach for auditory attention decoding, outperforming CSP methods especially with longer EEG decision windows.

Findings

RGC performs similarly to CSP for short decision lengths.

RGC significantly outperforms CSP for longer decision windows.

The method advances EEG-based auditory attention decoding techniques.

Abstract

Auditory attention decoding (AAD) algorithms decode the auditory attention from electroencephalography (EEG) signals that capture the listener's neural activity. Such AAD methods are believed to be an important ingredient towards so-called neuro-steered assistive hearing devices. For example, traditional AAD decoders allow detecting to which of multiple speakers a listener is attending to by reconstructing the amplitude envelope of the attended speech signal from the EEG signals. Recently, an alternative paradigm to this stimulus reconstruction approach was proposed, in which the directional focus of auditory attention is determined instead, solely based on the EEG, using common spatial pattern filters (CSP). Here, we propose Riemannian geometry-based classification (RGC) as an alternative for this CSP approach, in which the covariance matrix of a new EEG segment is directly classified while taking its Riemannian structure into account. While the proposed RGC method performs similarly to the CSP method for short decision lengths (i.e., the amount of EEG samples used to make a decision), we show that it significantly outperforms it for longer decision window lengths.