Noise-robust cortical tracking of attended speech in real-world acoustic scenes
Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is a...
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Published in | NeuroImage (Orlando, Fla.) Vol. 156; pp. 435 - 444 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
01.08.2017
Elsevier Limited |
Subjects | |
Online Access | Get full text |
ISSN | 1053-8119 1095-9572 1095-9572 |
DOI | 10.1016/j.neuroimage.2017.04.026 |
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Abstract | Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40–50s long blocks of data from 64 scalp electrodes were equally high (80–90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces.
•Selective attention to speech in real-world acoustic scenarios.•Cortical delta-theta activity entrains to attended speech in a noise-robust manner.•Cortex represents reverberant speech without its acoustic distortion.•Single-trial EEG decoding of selective attention is robust to acoustic noise. |
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AbstractList | Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40–50s long blocks of data from 64 scalp electrodes were equally high (80–90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces.
•Selective attention to speech in real-world acoustic scenarios.•Cortical delta-theta activity entrains to attended speech in a noise-robust manner.•Cortex represents reverberant speech without its acoustic distortion.•Single-trial EEG decoding of selective attention is robust to acoustic noise. Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40-50s long blocks of data from 64 scalp electrodes were equally high (80-90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces.Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40-50s long blocks of data from 64 scalp electrodes were equally high (80-90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces. Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40–50s long blocks of data from 64 scalp electrodes were equally high (80–90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces. |
Author | Fuglsang, Søren Asp Hjortkjær, Jens Dau, Torsten |
Author_xml | – sequence: 1 givenname: Søren Asp surname: Fuglsang fullname: Fuglsang, Søren Asp email: soerenf@elektro.dtu.dk organization: Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Building 352, 2800 Kgs. Lyngby, Denmark – sequence: 2 givenname: Torsten surname: Dau fullname: Dau, Torsten organization: Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Building 352, 2800 Kgs. Lyngby, Denmark – sequence: 3 givenname: Jens surname: Hjortkjær fullname: Hjortkjær, Jens email: jhjort@elektro.dtu.dk organization: Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Building 352, 2800 Kgs. Lyngby, Denmark |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28412441$$D View this record in MEDLINE/PubMed |
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Keywords | Acoustic simulations EEG Auditory attention Theta rhythms Speech Cortical entrainment Decoding Delta rhythms |
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SubjectTerms | Acoustic simulations Acoustic Stimulation Acoustics Adult Attention Attention - physiology Auditory attention Auditory Cortex - physiology Cortex Cortical entrainment Decoding Delta rhythms EEG Electrodes Electroencephalography Envelopes Female Humans Interfaces Male Noise Sound Speech Speech perception Speech Perception - physiology Statistical analysis Studies Theta rhythms Voice recognition Young Adult |
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