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Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG

Wilroth, Johanna ; Keding, Oskar LU orcid ; Skoglund, Martin A. ; Sandsten, Maria LU ; Enqvist, Martin and Alickovic, Emina (2026) In European Journal of Neuroscience 63(9).
Abstract
Everyday communication is dynamic and multisensory, often involving shifting attention, overlapping speech, and visual cues. Yet, most neural attention tracking studies are still limited to highly controlled lab settings, using clean, often audio‐only stimuli and requiring sustained attention to a single talker. This work addresses that gap by introducing a novel dataset from 24 normal‐hearing participants. We used a wearable electroencephalography (EEG) system (44 scalp electrodes and 20 cEEGrid electrodes) in an audiovisual (AV) paradigm with three conditions: sustained attention to a single talker in a two‐talker environment, attention switching between two talkers, and unscripted two‐talker conversations with a competing single talker.... (More)
Everyday communication is dynamic and multisensory, often involving shifting attention, overlapping speech, and visual cues. Yet, most neural attention tracking studies are still limited to highly controlled lab settings, using clean, often audio‐only stimuli and requiring sustained attention to a single talker. This work addresses that gap by introducing a novel dataset from 24 normal‐hearing participants. We used a wearable electroencephalography (EEG) system (44 scalp electrodes and 20 cEEGrid electrodes) in an audiovisual (AV) paradigm with three conditions: sustained attention to a single talker in a two‐talker environment, attention switching between two talkers, and unscripted two‐talker conversations with a competing single talker. Analysis included temporal response functions (TRFs) modeling, optimal lag analysis, selective attention classification with decision windows ranging from 1.1 to 35 s, and comparisons of TRFs for attention to AV conversations versus side audio‐only talkers. Key findings show significant differences in the attention‐related P2 peak between attended and ignored speech across conditions for scalp EEG. Interestingly, our results revealed strong cross‐condition generalization, with models trained in one condition maintaining good performance when evaluated on the other two. No significant change in performance between switching and sustained attention suggests robustness for attention switches. Optimal lag analysis revealed a narrower peak for conversation compared to single‐talker AV stimuli, reflecting the additional complexity of multi‐talker processing. Classification of selective attention was consistently above chance (5570 for scalp EEG, whereas cEEGrid data yielded lower correlations, highlighting the need for further methodological improvements. These results demonstrate that wearable EEG can reliably track selective attention in dynamic, multisensory listening scenarios and provide guidance for designing future AV paradigms and real‐world attention tracking applications. (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
European Journal of Neuroscience
volume
63
issue
9
article number
e70538
pages
16 pages
publisher
Wiley-Blackwell
external identifiers
  • pmid:42104679
  • scopus:105038160665
ISSN
1460-9568
DOI
10.1111/ejn.70538
language
English
LU publication?
yes
id
5ce17afd-6f4f-4ff3-9fb1-bb1e99498bd6
date added to LUP
2026-05-21 14:31:25
date last changed
2026-08-31 16:31:19
@article{5ce17afd-6f4f-4ff3-9fb1-bb1e99498bd6,
  abstract     = {{Everyday communication is dynamic and multisensory, often involving shifting attention, overlapping speech, and visual cues. Yet, most neural attention tracking studies are still limited to highly controlled lab settings, using clean, often audio‐only stimuli and requiring sustained attention to a single talker. This work addresses that gap by introducing a novel dataset from 24 normal‐hearing participants. We used a wearable electroencephalography (EEG) system (44 scalp electrodes and 20 cEEGrid electrodes) in an audiovisual (AV) paradigm with three conditions: sustained attention to a single talker in a two‐talker environment, attention switching between two talkers, and unscripted two‐talker conversations with a competing single talker. Analysis included temporal response functions (TRFs) modeling, optimal lag analysis, selective attention classification with decision windows ranging from 1.1 to 35 s, and comparisons of TRFs for attention to AV conversations versus side audio‐only talkers. Key findings show significant differences in the attention‐related P2 peak between attended and ignored speech across conditions for scalp EEG. Interestingly, our results revealed strong cross‐condition generalization, with models trained in one condition maintaining good performance when evaluated on the other two. No significant change in performance between switching and sustained attention suggests robustness for attention switches. Optimal lag analysis revealed a narrower peak for conversation compared to single‐talker AV stimuli, reflecting the additional complexity of multi‐talker processing. Classification of selective attention was consistently above chance (5570 for scalp EEG, whereas cEEGrid data yielded lower correlations, highlighting the need for further methodological improvements. These results demonstrate that wearable EEG can reliably track selective attention in dynamic, multisensory listening scenarios and provide guidance for designing future AV paradigms and real‐world attention tracking applications.}},
  author       = {{Wilroth, Johanna and Keding, Oskar and Skoglund, Martin A. and Sandsten, Maria and Enqvist, Martin and Alickovic, Emina}},
  issn         = {{1460-9568}},
  language     = {{eng}},
  number       = {{9}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{European Journal of Neuroscience}},
  title        = {{Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG}},
  url          = {{http://dx.doi.org/10.1111/ejn.70538}},
  doi          = {{10.1111/ejn.70538}},
  volume       = {{63}},
  year         = {{2026}},
}