Sample-level EEG-based Selective Auditory Attention Decoding with Markov Switching Models

TL;DR

This paper introduces a novel Markov switching model that integrates attention decoding and smoothing for EEG signals, enabling faster and more accurate sample-level detection of auditory attention switches.

Contribution

It presents a unified probabilistic framework for attention decoding and smoothing, improving temporal resolution without sacrificing accuracy.

Findings

Achieves comparable accuracy to HMM post-processing.

Enables faster detection of attention switches.

Provides a sample-level decoding approach.

Abstract

Selective auditory attention decoding aims to identify the speaker of interest from listeners' neural signals, such as electroencephalography (EEG), in the presence of multiple concurrent speakers. Most existing methods operate at the window level, facing a trade-off between temporal resolution and decoding accuracy. Recent work has shown that hidden Markov model (HMM)-based post-processing can smooth window-level decoder outputs to improve this trade-off. Instead of using a separate smoothing step, we propose to integrate the decoding and smoothing components into a single probabilistic framework using a Markov switching model (MSM). It directly models the relationship between the EEG and speech envelopes under each attention state while incorporating the temporal dynamics of attention. This formulation enables sample-level attention decoding, with model parameters and attention states jointly estimated via the expectation-maximization algorithm. Experimental results demonstrate that this integrated MSM formulation achieves comparable decoding accuracy to HMM post-processing while providing faster attention switch detection. The code for the proposed method is available at https://github.com/YYao-42/MSM.