# NeuroHeed: Neuro-Steered Speaker Extraction using EEG Signals

arXiv: https://arxiv.org/abs/2307.14303 · HTML: https://mlsanity.com/paper/2307.14303/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2023.

## Built on by (most cited first, 7 of 7)

- [Plug-and-Play Co-Occurring Face Attention for Robust Audio-Visual Speaker Extraction](https://mlsanity.com/paper/2505.20635/) (2025)
- [NeuroSpex: Neuro-Guided Speaker Extraction with Cross-Modal Attention](https://mlsanity.com/paper/2409.02489/) (2024)
- [MoMuSE: Momentum Multi-modal Target Speaker Extraction for Real-time Scenarios with Impaired Visual Cues](https://mlsanity.com/paper/2412.08247/) (2024)
- [TFGA-Net: Temporal-Frequency Graph Attention Network for Brain-Controlled Speaker Extraction](https://mlsanity.com/paper/2510.12275/) (2025)
- [Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training](https://mlsanity.com/paper/2606.24164/) (2026)
- [MAESTRO: a Multimodal Auditory-attention Egocentric Speech-TRacking Open corpus](https://mlsanity.com/paper/2609.31898/) (2026)
- [Merit: Making every modality earn its contribution in multimodal attention decoding](https://mlsanity.com/paper/2610.17507/) (2026)

## Abstract (arXiv)

Humans possess the remarkable ability to selectively attend to a single speaker amidst competing voices and background noise, known as selective auditory attention. Recent studies in auditory neuroscience indicate a strong correlation between the attended speech signal and the corresponding brain's elicited neuronal activities, which the latter can be measured using affordable and non-intrusive electroencephalography (EEG) devices. In this study, we present NeuroHeed, a speaker extraction model that leverages EEG signals to establish a neuronal attractor which is temporally associated with the speech stimulus, facilitating the extraction of the attended speech signal in a cocktail party scenario. We propose both an offline and an online NeuroHeed, with the latter designed for real-time inference. In the online NeuroHeed, we additionally propose an autoregressive speaker encoder, which accumulates past extracted speech signals for self-enrollment of the attended speaker information into an auditory attractor, that retains the attentional momentum over time. Online NeuroHeed extracts the current window of the speech signals with guidance from both attractors. Experimental results demonstrate that NeuroHeed effectively extracts brain-attended speech signals, achieving high signal quality, excellent perceptual quality, and intelligibility in a two-speaker scenario.