Brain scans reveal how we focus in messy, real-world conversations
Scientists have mapped the brain signals that control attention during natural conversation—the first time this has been measured outside a lab using portable EEG. The findings could improve hearing aids, design better workplace communication tools, and help people with attention disorders navigate noisy environments.
Originaltitel: Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG
Bärbar EEG-teknik kan spåra uppmärksamhet under naturliga samtal i realistiska miljöer — en kapacitet som saknas i dagens kliniska och kommersiella lösningar. Forskare vid Linköpings universitet använde 44 skalp-elektroder och 20 EEGrid-elektroder på 24 försöksdeltagare i tre scenarier: fokusering på en talare bland två, växling mellan talare och oscenarierad tvåsamtal. Modeller tränade på ett villkor bibehöll god prestanda på andra villkor (55–70 procent klassificeringsacceleration för skalp-EEG), medan EEGrid-data gav svagare korrelationer. Resultaten visar att bärbar EEG överensstämmer med naturlig kommunikation utan prestation att försämras mellan uppmärksamhetsväxling och fokuserad lyssning. För inköp av neuromonitoring-system och utvecklare av hörapparater öppnar detta vägen till objektiv, bärbar uppmärksamhetsmätning i klinisk miljö — kritiskt för audiologisk bedömning och personalanpassning av hörteknik utan laboratoriekontroll.
<p>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 (55%-70% accuracy) 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.</p>