Ear-EEG Measures of Auditory Attention to Continuous Speech

Auditory attention is an important cognitive function used to separate relevant from irrelevant auditory information. However, most findings on attentional selection have been obtained in highly controlled laboratory settings using bulky recording setups and unnaturalistic stimuli. Recent advances in electroencephalography (EEG) facilitate the measurement of brain activity outside the laboratory, and around-the-ear sensors such as the cEEGrid promise unobtrusive acquisition. In parallel, methods such as speech envelope tracking, intersubject correlations and spectral entropy measures emerged which allow us to study attentional effects in the neural processing of natural, continuous auditory scenes. In the current study, we investigated whether these three attentional measures can be reliably obtained when using around-the-ear EEG. To this end, we analyzed the cEEGrid data of 36 participants who attended to one of two simultaneously presented speech streams. Speech envelope tracking results confirmed a reliable identification of the attended speaker from cEEGrid data. The accuracies in identifying the attended speaker increased when fitting the classification model to the individual. Artifact correction of the cEEGrid data with artifact subspace reconstruction did not increase the classification accuracy. Intersubject correlations were higher for those participants attending to the same speech stream than for those attending to different speech streams, replicating previously obtained results with high-density cap-EEG. We also found that spectral entropy decreased over time, possibly reflecting the decrease in the listener's level of attention. Overall, these results support the idea of using ear-EEG measurements to unobtrusively monitor auditory attention to continuous speech. This knowledge may help to develop assistive devices that support listeners separating relevant from irrelevant information in complex auditory environments.

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG

OBJECTIVE

Neuro-steered assistive technologies have been suggested to offer a major advancement in future devices like neuro-steered hearing aids. Auditory attention decoding methods would in that case allow for identification of an attended speaker within complex auditory environments, exclusively from neural data. Decoding the attended speaker using neural information has so far only been done in controlled laboratory settings. Yet, it is known that ever-present factors like distraction and movement are reflected in the neural signal parameters related to attention.

APPROACH

Thus, in the current study we applied a two-competing speaker paradigm to investigate performance of a commonly applied EEG-based auditory attention decoding (AAD) model outside of the laboratory during leisure walking and distraction. Unique environmental sounds were added to the auditory scene and served as distractor events.

MAIN RESULTS

The current study shows, for the first time, that the attended speaker can be accurately decoded during natural movement. At a temporal resolution of as short as 5-seconds and without artifact attenuation, decoding was found to be significantly above chance level. Further, as hypothesized, we found a decrease in attention to the to-be-attended and the to-be-ignored speech stream after the occurrence of a salient event. Additionally, we demonstrate that it is possible to predict neural correlates of distraction with a computational model of auditory saliency based on acoustic features.

CONCLUSION

Taken together, our study shows that auditory attention tracking outside of the laboratory in ecologically valid conditions is feasible and a step towards the development of future neural-steered hearing aids.

Are They Calling My Name? Attention Capture Is Reflected in the Neural Tracking of Attended and Ignored Speech

Difficulties in selectively attending to one among several speakers have mainly been associated with the distraction caused by ignored speech. Thus, in the current study, we investigated the neural processing of ignored speech in a two-competing-speaker paradigm. For this, we recorded the participant’s brain activity using electroencephalography (EEG) to track the neural representation of the attended and ignored speech envelope. To provoke distraction, we occasionally embedded the participant’s first name in the ignored speech stream. Retrospective reports as well as the presence of a P3 component in response to the name indicate that participants noticed the occurrence of their name. As predicted, the neural representation of the ignored speech envelope increased after the name was presented therein, suggesting that the name had attracted the participant’s attention. Interestingly, in contrast to our hypothesis, the neural tracking of the attended speech envelope also increased after the name occurrence. On this account, we conclude that the name might not have primarily distracted the participants, at most for a brief duration, but that it alerted them to focus to their actual task. These observations remained robust even when the sound intensity of the ignored speech stream, and thus the sound intensity of the name, was attenuated.

Decoding the Attended Speaker From EEG Using Adaptive Evaluation Intervals Captures Fluctuations in Attentional Listening

Listeners differ in their ability to attend to a speech stream in the presence of a competing sound. Differences in speech intelligibility in noise cannot be fully explained by the hearing ability which suggests the involvement of additional cognitive factors. A better understanding of the temporal fluctuations in the ability to pay selective auditory attention to a desired speech stream may help in explaining these variabilities. In order to better understand the temporal dynamics of selective auditory attention, we developed an online auditory attention decoding (AAD) processing pipeline based on speech envelope tracking in the electroencephalogram (EEG). Participants had to attend to one audiobook story while a second one had to be ignored. Online AAD was applied to track the attention toward the target speech signal. Individual temporal attention profiles were computed by combining an established AAD method with an adaptive staircase procedure. The individual decoding performance over time was analyzed and linked to behavioral performance as well as subjective ratings of listening effort, motivation, and fatigue. The grand average attended speaker decoding profile derived in the online experiment indicated performance above chance level. Parameters describing the individual AAD performance in each testing block indicated significant differences in decoding performance over time to be closely related to the behavioral performance in the selective listening task. Further, an exploratory analysis indicated that subjects with poor decoding performance reported higher listening effort and fatigue compared to good performers. Taken together our results show that online EEG based AAD in a complex listening situation is feasible. Adaptive attended speaker decoding profiles over time could be used as an objective measure of behavioral performance and listening effort. The developed online processing pipeline could also serve as a basis for future EEG based near real-time auditory neurofeedback systems.

Decoding Selective Attention in Normal Hearing Listeners and Bilateral Cochlear Implant Users With Concealed Ear EEG

Electroencephalography (EEG) data can be used to decode an attended speech source in normal-hearing (NH) listeners using high-density EEG caps, as well as around-the-ear EEG devices. The technology may find application in identifying the target speaker in a cocktail party like scenario and steer speech enhancement algorithms in cochlear implants (CIs). However, the worse spectral resolution and the electrical artifacts introduced by a CI may limit the applicability of this approach to CI users. The goal of this study was to investigate whether selective attention can be decoded in CI users using an around-the-ear EEG system (cEEGrid). The performances of high-density cap EEG recordings and cEEGrid EEG recordings were compared in a selective attention paradigm using an envelope tracking algorithm. Speech from two audio books was presented through insert earphones to NH listeners and via direct audio cable to the CI users. 10 NH listeners and 10 bilateral CI users participated in the study. Participants were instructed to attend to one out of the two concurrent speech streams while data were recorded by a 96-channel scalp EEG and an 18-channel cEEGrid setup simultaneously. Reconstruction performance was evaluated by means of parametric correlations between the reconstructed speech and both, the envelope of the attended and the unattended speech stream. Results confirm the feasibility to decode selective attention by means of single-trial EEG data in NH and CI users using a high-density EEG. All NH listeners and 9 out of 10 CI achieved high decoding accuracies. The cEEGrid was successful in decoding selective attention in 5 out of 10 NH listeners. The same result was obtained for CI users.

Effects of directional sound processing and listener's motivation on EEG responses to continuous noisy speech: Do normal-hearing and aided hearing-impaired listeners differ?

OBJECTIVE

It has been suggested that the next major advancement in hearing aid (HA) technology needs to include cognitive feedback from the user to control HA functionality. In order to enable automatic brainwave-steered HA adjustments, attentional processes underlying speech-in-noise perception in aided hearing-impaired individuals need to be better understood. Here, we addressed the influence of two important factors for the listening performance of HA users - hearing aid processing and motivation - by analysing ongoing neural responses during long-term listening to continuous noisy speech.

METHODS

Sixteen normal-hearing (NH) and 15 linearly aided hearing-impaired (aHI) participants listened to an audiobook recording embedded in realistic speech babble noise at individually adjusted signal-to-noise ratios (SNRs). A HA simulator was used for simulating a directional microphone setting as well as for providing individual amplification. To assess listening performance behaviourally, participants answered questions about the contents of the audiobook. We manipulated (1) the participants' motivation by offering a monetary reward for good listening performance in one half of the measurements and (2) the SNR by engaging/disengaging the directional microphone setting. During the speech-in-noise task, electroencephalography (EEG) signals were recorded using wireless, mobile hardware. EEG correlates of listening performance were investigated using EEG impulse responses, as estimated using the cross-correlation between the recorded EEG signal and the temporal envelope of the audiobook at the output of the HA simulator.

RESULTS

At the behavioural level, we observed better performance for the NH listeners than for the aHI listeners. Furthermore, the directional microphone setting led to better performance for both participant groups, and when the directional microphone setting was disengaged motivation also improved the performance of the aHI participants. Analysis of the EEG impulse responses showed faster N1P2 responses for both groups and larger N2 peak amplitudes for the aHI group when the directional microphone setting was activated, but no physiological correlates of motivation.

SIGNIFICANCE

The results of this study indicate that motivation plays an important role for speech understanding in noise. In terms of neuro-steered HAs, our results suggest that the latency of attentional processes is influenced by HA-induced stimulus changes, which can potentially be used for inferring benefit from noise suppression processing automatically. Further research is necessary to identify the neural correlates of motivation as an exclusive top-down process and to combine such features with HA-driven ones for online HA adjustments.

Toward Decoding Selective Attention From Single-Trial EEG Data in Cochlear Implant Users

Previous results showed that it is possible to decode an attended speech source from EEG data via the reconstruction of the speech envelope in normal hearing (NH) listeners. However, so far it is unknown that how the performance of such a decoder is affected by the decrease in spectral resolution and the electrical artifacts introduced by a cochlear implant (CI) in users of these prostheses. NH listeners and bilateral CI users participated in the present study. Speeches from two audio books, one uttered by a male voice and one by a female voice, were presented to NH listeners and CI users. Participants were instructed to attend to one of the two speech streams presented dichotically while a 96-channel EEG was recorded. Speech envelope reconstruction from the EEG data was obtained by training decoders using a regularized least square estimation method. Decoding accuracy was defined as the percentage of accurately reconstructed trials for each subject. For NH listeners, the experiment was repeated using a vocoder to reduce spectral resolution and simulate speech perception with a CI in NH listeners. The results showed a decoding accuracy of 80.9 % using the original sound files in NH listeners. The performance dropped to 73.2 % in the vocoder condition and to 71.5 % in the group of CI users. In sum, although the accuracy drops when the spectral resolution becomes worse, the results show the feasibility to decode the attended sound source in NH listeners with a vocoder simulation, and even in CI users, albeit more training data are needed.

Asperger's syndrome: What to consider?

Asperger's syndrome is a neurodevelopmental disorder which is part of the large family of autism spectrum disorders. People with Asperger's syndrome have difficulties in social interactions, verbal and non-verbal communication, and may display behavioural oddities, with stereotypies and limited interests. They show no language delay and their cognitive development is not marked by an overall delay but by specific impairments in certain areas such as the executive functions. The clinical presentations are very heterogeneous, varying according to age and psychiatric comorbidities. Screening, diagnosis and specialized treatment are not made any easier by the diversity of the clinical manifestations. Asperger's syndrome is often diagnosed belatedly, at 11years of age on average and even in adulthood in some cases. This late diagnosis has a significant impact on the risks of depression and a poor quality of life. However, in adulthood or in adolescence, certain situations, personality traits and cognitive profiles or certain comorbidities should suggest the hypothesis of an Asperger-type autism spectrum disorder. We propose here a review of the clinical situations at different ages of life that could help with the screening and the referral of patients to specialized clinicians for diagnosis and appropriate treatment.

Anaerobic bacteria cultured from cystic fibrosis airways correlate to milder disease: a multisite study

Anaerobic and aerobic bacteria were quantitated in respiratory samples across three cystic fibrosis (CF) centres using extended culture methods. Subjects aged 1-69 years who were clinically stable provided sputum (n=200) or bronchoalveolar lavage (n=55). 18 anaerobic and 39 aerobic genera were cultured from 59% and 95% of samples, respectively; 16 out of 57 genera had a ≥5% prevalence across centres.Analyses of microbial communities using co-occurrence networks in sputum samples showed groupings of oral, including anaerobic, bacteria, whereas typical CF pathogens formed distinct entities. Pseudomonas was associated with worse nutrition and F508del genotype, whereas anaerobe prevalence was positively associated with pancreatic sufficiency, better nutrition and better lung function. A higher total anaerobe/total aerobe CFU ratio was associated with pancreatic sufficiency and better nutrition. Subjects grouped by factor analysis who had relative dominance of anaerobes over aerobes had milder disease compared with a Pseudomonas-dominated group with similar proportions of subjects that were homozygous for F508del.In summary, anaerobic bacteria occurred at an early age. In sputum-producing subjects anaerobic bacteria were associated with milder disease, suggesting that targeted eradication of anaerobes may not be warranted in sputum-producing CF subjects.

Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison

OBJECTIVE

This study presents a direct comparison of a classical EEG cap setup with a new around-the-ear electrode array (cEEGrid) to gain a better understanding of the potential of ear-centered EEG.

APPROACH

Concurrent EEG was recorded from a classical scalp EEG cap and two cEEGrids that were placed around the left and the right ear. Twenty participants performed a spatial auditory attention task in which three sound streams were presented simultaneously. The sound streams were three seconds long and differed in the direction of origin (front, left, right) and the number of beats (3, 4, 5 respectively), as well as the timbre and pitch. The participants had to attend to either the left or the right sound stream.

MAIN RESULTS

We found clear attention modulated ERP effects reflecting the attended sound stream for both electrode setups, which agreed in morphology and effect size. A single-trial template matching classification showed that the direction of attention could be decoded significantly above chance (50%) for at least 16 out of 20 participants for both systems. The comparably high classification results of the single trial analysis underline the quality of the signal recorded with the cEEGrids.

SIGNIFICANCE

These findings are further evidence for the feasibility of around the-ear EEG recordings and demonstrate that well described ERPs can be measured. We conclude that concealed behind-the-ear EEG recordings can be an alternative to classical cap EEG acquisition for auditory attention monitoring.

Target Speaker Detection with Concealed EEG Around the Ear

Target speaker identification is essential for speech enhancement algorithms in assistive devices aimed toward helping the hearing impaired. Several recent studies have reported that target speaker identification is possible through electroencephalography (EEG) recordings. If the EEG system could be reduced to acceptable size while retaining the signal quality, hearing aids could benefit from the integration with concealed EEG. To compare the performance of a multichannel around-the-ear EEG system with high-density cap EEG recordings an envelope tracking algorithm was applied in a competitive speaker paradigm. The data from 20 normal hearing listeners were concurrently collected from the traditional state-of-the-art laboratory wired EEG system and a wireless mobile EEG system with two bilaterally-placed around-the-ear electrode arrays (cEEGrids). The results show that the cEEGrid ear-EEG technology captured neural signals that allowed the identification of the attended speaker above chance-level, with 69.3% accuracy, while cap-EEG signals resulted in the accuracy of 84.8%. Further analyses investigated the influence of ear-EEG signal quality and revealed that the envelope tracking procedure was unaffected by variability in channel impedances. We conclude that the quality of concealed ear-EEG recordings as acquired with the cEEGrid array has potential to be used in the brain-computer interface steering of hearing aids.

Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications

OBJECTIVE

Recent studies have provided evidence that temporal envelope driven speech decoding from high-density electroencephalography (EEG) and magnetoencephalography recordings can identify the attended speech stream in a multi-speaker scenario. The present work replicated the previous high density EEG study and investigated the necessary technical requirements for practical attended speech decoding with EEG.

APPROACH

Twelve normal hearing participants attended to one out of two simultaneously presented audiobook stories, while high density EEG was recorded. An offline iterative procedure eliminating those channels contributing the least to decoding provided insight into the necessary channel number and optimal cross-subject channel configuration. Aiming towards the future goal of near real-time classification with an individually trained decoder, the minimum duration of training data necessary for successful classification was determined by using a chronological cross-validation approach.

MAIN RESULTS

Close replication of the previously reported results confirmed the method robustness. Decoder performance remained stable from 96 channels down to 25. Furthermore, for less than 15 min of training data, the subject-independent (pre-trained) decoder performed better than an individually trained decoder did.

SIGNIFICANCE

Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.

Aspergillus-associated airway disease, inflammation, and the innate immune response

Aspergillus moulds exist ubiquitously as spores that are inhaled in large numbers daily. Whilst most are removed by anatomical barriers, disease may occur in certain circumstances. Depending on the underlying state of the human immune system, clinical consequences can ensue ranging from an excessive immune response during allergic bronchopulmonary aspergillosis to the formation of an aspergilloma in the immunocompetent state. The severest infections occur in those who are immunocompromised where invasive pulmonary aspergillosis results in high mortality rates. The diagnosis of Aspergillus-associated pulmonary disease is based on clinical, radiological, and immunological testing. An understanding of the innate and inflammatory consequences of exposure to Aspergillus species is critical in accounting for disease manifestations and preventing sequelae. The major components of the innate immune system involved in recognition and removal of the fungus include phagocytosis, antimicrobial peptide production, and recognition by pattern recognition receptors. The cytokine response is also critical facilitating cell-to-cell communication and promoting the initiation, maintenance, and resolution of the host response. In the following review, we discuss the above areas with a focus on the innate and inflammatory response to airway Aspergillus exposure and how these responses may be modulated for therapeutic benefit.