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

arXiv: https://arxiv.org/abs/2010.07171 · HTML: https://mlsanity.com/paper/2010.07171/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection](https://mlsanity.com/paper/2410.11181/) (2024)
- [A DenseNet-based method for decoding auditory spatial attention with EEG](https://mlsanity.com/paper/2309.07690/) (2023)
- [TAnet: A New Temporal Attention Network for EEG-based Auditory Spatial Attention Decoding with a Short Decision Window](https://mlsanity.com/paper/2401.05819/) (2024)
- [Multi-class Decoding of Attended Speaker Direction Using Electroencephalogram and Audio Spatial Spectrum](https://mlsanity.com/paper/2411.06928/) (2024)

## Abstract (arXiv)

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.