Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Июль 14, 2023

Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks even irreversible damage to soft tissues. Here, we propose in-ear visual auditory BCIs based on bioelectronics, named as SpiralE, can adaptively expand spiral along the meatus under electrothermal actuation ensure conformal contact. Participants achieve offline accuracies of 95% 9-target steady state evoked potential (SSVEP) BCI classification type target phrases successfully a calibration-free 40-target online SSVEP speller experiment. Interestingly, SSVEPs exhibit significant 2

Язык: Английский

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Predictors for estimating subcortical EEG responses to continuous speech

PLoS ONE, Год журнала: 2024, Номер 19(2), С. e0297826 - e0297826

Опубликована: Фев. 8, 2024

Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing stimuli. The role these processing can be investigated by measuring their electrical activity using scalp-mounted electrodes. However, typical analysis methods involve averaging neural responses to many short repetitive stimuli bear little relevance daily listening environments. Recently, subcortical more ecologically relevant continuous were detected linear encoding models. These estimate temporal response function (TRF), which is a regression model minimises error between measured signal predictor derived from stimulus. Using predictors highly non-linear peripheral system may improve TRF estimation accuracy peak detection. Here, we compare both simple complex models for estimating TRFs on electroencephalography (EEG) data 24 participants speech. We also investigate length required TRFs, find around 12 minutes sufficient clear wave V peaks (>3 dB SNR) seen nearly all participants. Interestingly, filterbank-based yield SNRs are not significantly different those estimated nerve, provided nonlinear effects adaptation appropriately modelled. Crucially, computing simpler than 50 times faster compared model. This work paves way efficient modelling detection speech, lead improved diagnosis metrics hearing impairment assistive technology.

Язык: Английский

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Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions

eLife, Год журнала: 2023, Номер 12

Опубликована: Ноя. 29, 2023

Even though human experience unfolds continuously in time, it is not strictly linear; instead, entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a varying acoustic signal into phonemes, words, and meaning, these levels all have distinct but interdependent temporal Time-lagged regression using response functions (TRFs) has recently emerged as promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind analysis easy accessible. We demonstrate its use, continuous sample paradigm, with freely available EEG dataset audiobook listening. A companion GitHub repository provides complete source code analysis, from raw data group-level statistics. More generally, advocate hypothesis-driven approach experimenter specifies hierarchy time-continuous representations that are hypothesized contributed responses, uses those predictor variables signal. This analogous multiple problem, addition time dimension. TRF decomposes associated different by estimating multivariate (mTRF), quantifying influence each on function time(-lags). allows asking two questions about variables: (1) Is there significant neural representation corresponding variable? And if so, (2) what characteristics it? Thus, can be systematically combined evaluated jointly model processing at levels. discuss applications approach, including potential linking algorithmic/representational theories through computational appropriate hypotheses.

Язык: Английский

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Exploring neural tracking of acoustic and linguistic speech representations in individuals with post‐stroke aphasia

Human Brain Mapping, Год журнала: 2024, Номер 45(8)

Опубликована: Май 26, 2024

Abstract Aphasia is a communication disorder that affects processing of language at different levels (e.g., acoustic, phonological, semantic). Recording brain activity via Electroencephalography while people listen to continuous story allows analyze responses acoustic and linguistic properties speech. When the neural aligns with these speech properties, it referred as tracking. Even though measuring tracking may present an interesting approach studying aphasia in ecologically valid way, has not yet been investigated individuals stroke‐induced aphasia. Here, we explored representations chronic phase after stroke age‐matched healthy controls. We found decreased (envelope envelope onsets) In addition, word surprisal displayed amplitudes around 195 ms over frontal electrodes, although this effect was corrected for multiple comparisons. These results show there potential capture impairments by However, more research needed validate results. Nonetheless, exploratory study shows naturalistic, presents powerful

Язык: Английский

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A tradeoff between acoustic and linguistic feature encoding in spoken language comprehension

eLife, Год журнала: 2023, Номер 12

Опубликована: Июль 7, 2023

When we comprehend language from speech, the phase of neural response aligns with particular features speech input, resulting in a phenomenon referred to as

Язык: Английский

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Cortical speech tracking is related to individual prediction tendencies

Cerebral Cortex, Год журнала: 2023, Номер 33(11), С. 6608 - 6619

Опубликована: Янв. 9, 2023

Listening can be conceptualized as a process of active inference, in which the brain forms internal models to integrate auditory information complex interaction bottom-up and top-down processes. We propose that individuals vary their "prediction tendency" this variation contributes experiential differences everyday listening situations shapes cortical processing acoustic input such speech. Here, we presented tone sequences varying entropy level, independently quantify prediction tendency (as anticipate low-level features) for each individual. This measure was then used predict speech tracking multi speaker task, where participants listened audiobooks narrated by target isolation or interfered 1 2 distractors. Furthermore, semantic violations were introduced into story, also examine effects word surprisal during processing. Our results show is related tendency. In addition, find interactions between background noise well disparate regions. findings suggest individual tendencies are generalizable across different may serve valuable element explain interindividual natural situations.

Язык: Английский

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Eye movements track prioritized auditory features in selective attention to natural speech

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Май 1, 2024

Abstract Over the last decades, cognitive neuroscience has identified a distributed set of brain regions that are critical for attention. Strong anatomical overlap with oculomotor processes suggests joint network attention and eye movements. However, role this shared in complex, naturalistic environments remains understudied. Here, we investigated movements relation to (un)attended sentences natural speech. Combining simultaneously recorded tracking magnetoencephalographic data temporal response functions, show gaze tracks attended speech, phenomenon termed ocular speech tracking. Ocular even differentiates target from distractor multi-speaker context is further related intelligibility. Moreover, provide evidence its contribution neural differences processing, emphasizing necessity consider activity future research interpretation auditory cognition.

Язык: Английский

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Neural tracking of linguistic and acoustic speech representations decreases with advancing age

NeuroImage, Год журнала: 2022, Номер 267, С. 119841 - 119841

Опубликована: Дек. 28, 2022

Background: Older adults process speech differently, but it is not yet clear how aging affects different levels of processing natural, continuous speech, both in terms bottom-up acoustic analysis and top-down generation linguistic-based predictions. We studied natural across the adult lifespan via electroencephalography (EEG) measurements neural tracking. Goals: Our goals are to analyze unique contribution linguistic using while controlling for influence processing. Moreover, we also age. In particular, focus on changes spatial temporal activation patterns response lifespan. Methods: 52 normal-hearing between 17 82 years age listened a naturally spoken story EEG signal was recorded. investigated effect speech. Because correlated with hearing capacity measures cognition, whether observed mediated by these factors. Furthermore, there an hemisphere lateralization spatiotemporal responses. Results: results showed that declines advancing as increased, latency certain aspects increased. Also tracking (NT) decreased increasing age, which at odds literature. contrast processing, older subjects shorter latencies early responses No evidence found hemispheric neither younger nor during Most effects were explained age-related decline or cognition. However, our suggest decreasing word-level partially due cognition than robust Conclusion: Spatial characteristics change These may be traces structural and/or functional occurs

Язык: Английский

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The effects of data quantity on performance of temporal response function analyses of natural speech processing

Frontiers in Neuroscience, Год журнала: 2023, Номер 16

Опубликована: Янв. 12, 2023

In recent years, temporal response function (TRF) analyses of neural activity recordings evoked by continuous naturalistic stimuli have become increasingly popular for characterizing properties within the auditory hierarchy. However, despite this rise in TRF usage, relatively few educational resources these tools exist. Here we use a dual-talker speech paradigm to demonstrate how key parameter experimental design, quantity acquired data, influences fit either individual data (subject-specific analyses), or group (generic analyses). We show that although model prediction accuracy increases monotonically with quantity, amount required achieve significant accuracies can vary substantially based on whether fitted contains densely (e.g., acoustic envelope) sparsely lexical surprisal) spaced features, especially when goal is capture aspect responses uniquely explained specific features. Moreover, generic models exhibit high performance small amounts test (2–8 min), if they are trained sufficiently large set. As such, may be particularly useful clinical and multi-task study designs limited recording time. Finally, regularization procedure used fitting interact models, larger training quantities resulting systematically amplitudes. Together, demonstrations work should aid new users analyses, combination other tools, such as piloting power serve detailed reference choosing acquisition duration future studies.

Язык: Английский

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Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

IEEE Transactions on Biomedical Engineering, Год журнала: 2022, Номер 70(1), С. 88 - 96

Опубликована: Июнь 21, 2022

The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including speech. TRFs based on speech envelopes typically have distinct components that provided remarkable insights into the cortical processing However, current methods may lead less than reliable estimates single-subject TRF components. Here, we compare two established methods, in component estimation, and also propose novel algorithms utilize prior knowledge these components, bypassing full estimation.

Язык: Английский

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