ArticleFrontiers in neuroscience2018
Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- From Selective Listening to Brain-Controlled Hearing: A Perspective on the Future of Auditory Technology.Journal of the Association for Research in Otolaryngology : JARO · 2026Review
- Closing the Loop on the Cocktail Party Effect: From Attention Decoding to Neuro-Steered Selective Hearing.Biology · 2026Article
- Competing speech streams are simultaneously represented in the human cortex during attention switching.PLoS biology · 2026Article
- Attention, musicality, and familiarity shape cortical speech tracking at the musical cocktail party.Brain and language · 2025Article
- VARX Granger analysis: Models for neuroscience, physiology, sociology and econometrics.PloS one · 2025Article
- Temporal coherence shapes cortical responses to speech mixtures in a ferret cocktail party.Communications biology · 2024Article
- Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.PLoS computational biology · 2024Article
- Temporal Coherence Shapes Cortical Responses to Speech Mixtures in a Ferret Cocktail Party.bioRxiv : the preprint server for biology · 2024Article
- Auditory-GAN: deep learning framework for improved auditory spatial attention detection.PeerJ. Computer science · 2024Article
- Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task.Scientific reports · 2023Article
- Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions.eLife · 2023Article
- Attentional Modulation of the Cortical Contribution to the Frequency-Following Response Evoked by Continuous Speech.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Target of selective auditory attention can be robustly followed with MEG.Scientific reports · 2023Article
- Affective Neural Responses Sonified through Labeled Correlation Alignment.Sensors (Basel, Switzerland) · 2023Article
- EEG alpha and pupil diameter reflect endogenous auditory attention switching and listening effort.The European journal of neuroscience · 2022Article
- Decoding Object-Based Auditory Attention from Source-Reconstructed MEG Alpha Oscillations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies.PLoS computational biology · 2021Article
- Dynamic selective auditory attention detection using RNN and reinforcement learning.Scientific reports · 2021Article
- EEG-based detection of the locus of auditory attention with convolutional neural networks.eLife · 2021Article
- Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment.Sensors (Basel, Switzerland) · 2021Article
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6 authors.
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Abstract
Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ
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