Biology subjects

Jehn, C.

Publications and source records attributed to Jehn, C..

4 recordsLinked to original sources

bioRxiv2026

CORSICA: Reproducible Suppression of Cochlear Implant Artifacts in EEG evoked by Continuous Speech

ObjectiveElectroencephalography (EEG) is a key tool for studying auditory processing in cochlear implant (CI) users. In particular, EEG recordings obtained during continuous speech are becoming increasingly important for assessing speech and language processing in CI users, and may be utilized for neurofeedback. However, CIs also induce strong stimulation artifacts that are time-locked to the stimulus and mask the neural responses that have smaller magnitudes. Existing artifact reduction methods are typically based on event-related potentials (ERPs) or require manual component selection, making them unsuitable for naturalistic listening conditions or large datasets. ApproachWe develop CORSICA (CORrelation-baSed ICA artifact rejection), a reproducible, parameter-efficient method for CI artifact reduction in EEG responses to continuous speech. CORSICA operates on independent components (ICs) obtained through Infomax ICA and requires no manual component labelling, with performance governed by a single tunable threshold. It exploits the observation that CI artifacts temporally follow the audio signal without delay, whereas neural responses have an inherent lag due to auditory pathway latencies. For each IC, CORSICA computes the cross-correlation with the speech stimulus. Artifacts are identified by a high signal-to-noise ratio (SNR) of the correlation peak near zero lag, and the component is rejected if this SNR exceeds a threshold. To benchmark CORSICA, we evaluate two alternatives: a TRF-based SNR method, in which temporal response functions are fitted to each IC and artifact-driven peaks near zero lag are used for rejection, and a variant replacing ICA with second-order blind identification (SOBI) as the source separation step. Main resultsCORSICA effectively suppressed CI artifacts while preserving neural activity, enabling recovery of physiologically plausible TRFs with only 2% of ICs rejected. Both benchmark methods confirmed the validity of the SNR-based rejection framework, but CORSICA outperformed the TRF-based alternative in artifact suppression quality. Replacing ICA with SOBI as the source separation step required more ICs to be rejected, further supporting ICA as the preferred backbone for CORSICA. SignificanceCORSICA provides a fully objective, label-free approach to identifying CI artifacts in speech-evoked EEG data, with no manual intervention required. By centering artifact rejection on a single interpretable threshold, it offers a reproducible preprocessing standard for future EEG studies on speech processing in CI users. ConclusionOur findings demonstrate that objective CI artifact suppression in speech-evoked EEG data is feasible on the basis of the ICs temporal response patterns.

neuroscience↗

bioRxiv2026

Deep Learning Reveals Cross-Modal Neural Representations of Auditory and Visual Mental Imagery in MEG

Mental imagery provides a unique window into the brains ability to internally simulate sensory experiences, offering valuable insights for both cognitive neuroscience and brain-computer interface (BCI) research. This study examined the neural representations of imagined auditory and visual stimuli using magnetoen-cephalography (MEG) and assessed the ability of machine learning models to decode these mental processes. MEG data were recorded from 18 right-handed participants during auditory and visual imagery tasks and source-reconstructed within modality-specific cortical regions of interest. We compared a convolutional neural network (CNN) and a linear logistic regression model within a subject-specific classification frame-work. Both approaches achieved above-chance decoding accuracies, with the CNN outperforming the linear model in the auditory task, whereas the linear model showed slightly higher accuracy for visual imagery. Notably, the CNN achieved significant decoding performance even when trained on non-task-relevant cortical regions, indicating that imagined stimuli are represented in distributed and partially overlapping neural networks across modalities. This cross-modal decoding capability highlights the potential of deep learning models to capture complex, multimodal neural patterns and suggests that future brain-computer interfaces could benefit from integrating auditory and visual information. A secondary, behavioral analysis revealed correlation of memory capacity and individual learning preferences with decoding performances, suggesting that individual cognitive differences may further shape the quality of neural representations. Together, these findings advance our understanding of cross-modal mental imagery and point toward more flexible and personalized approaches in BCI design. New and NoteworthyBy comparing linear and deep classifiers, this work shows that convolutional networks capture rich, cross-modal neural representations of auditory and visual mental imagery in MEG. Significant decoding from non-task-relevant regions indicates distributed cortical engagement, highlighting deep learnings potential for robust, modality-independent brain-computer interfaces.

neuroscience↗

bioRxiv2026

Talking avatars can differentially modulate cortical speech tracking in the high and in the low delta band

In noisy listening environments, visual cues from a speakers face can significantly boost speech compre-hension. The underlying audiovisual integration in the brain involves neural tracking of audiovisual speech features. Moreover, lip reading in silence is associated with tracking of the speech envelope in the low-delta frequency band (0.5 - 1 Hz). Recently, digital avatars have emerged that can support speech comprehen-sion. Yet, it remains unclear how the human brain integrates such artificial visual signals with natural speech. Here, we employed magnetoencephalography (MEG) to measure the neural response to a natural video, an avatar generated by deep neural networks, and a degraded video serving as a control. We demonstrate that the avatar can enhance speech-in-noise comprehension to a similar degree as the degraded video, although less than the natural video. We further identify a late response at 600 ms in the neural tracking of the audi-tory cortex in the high delta band (1 - 4 Hz) that predicts audiovisual speech comprehension. In contrast, we found that neural tracking in the low delta band is related to silent lip-reading performance. Importantly, the tracking in the low delta band evoked by the avatars is much weaker and occurs earlier than that elicited by the other audiovisual stimuli. Neural tracking in the theta band (4 - 8 Hz) is not involved in audiovisual integration. Our results show that the low delta band and the high delta band play clearly distinct roles in visual-only and audiovisual speech processing, and suggest potential avenues for further boosting the abilities of avatars to support speech comprehension. Significance StatementUnderstanding a conversational partner is essential for everyday communication. Yet, many people -- due to aging or other factors -- struggle to follow speech in noisy environments. Seeing the speakers face can greatly enhance speech comprehension, but visual cues are often unavailable, such as during public announcements or telephone conversations. Digital avatars offer a promising alternative, but how the brain integrates audiovisual information from such artificial sources remains unclear. Using magnetoencephalog-raphy (MEG), we investigated how the brain processes and integrates speech when visual information is provided by either natural or artificial (avatar-based) signals. Our findings reveal both shared and distinct neural mechanisms of audiovisual integration, providing critical insight into how visual input can support speech understanding in challenging listening conditions.

neuroscience↗

bioRxiv2025

Attention Decoding at the Cocktail Party: Preserved in Hearing Aid Users, Reduced in Cochlear Implant Users

AbstractUsers of hearing aids (HAs) and cochlear implants (CIs) experience significant difficulty understanding a target speaker in multi-talker environments or when other background noise is present. Segregation of a particular voice from background noise occurs partly through enhanced cortical tracking of amplitude fluctuations in the target signal. Measuring a persons cortical tracking allows decoding their focus of attention and may be used for neurofeedback in hearing devices, potentially aiding their users with speech-in-noise comprehension. Most studies on cortical speech tracking have employed typical hearing (TH) individuals, whereas studies in people with hearing impairment whose cortical tracking may differ are still scarce. The objective of this study was to compare cortical speech tracking of HA (n=29) and CI users (n=24) to that of age-matched TH individuals (n=29). We recorded EEG data while the participants attended one of two competing talkers (one with a female and one with a male voice), in a free-field acoustic environment. Importantly, HA users as well as CI users used their personal, clinically-fitted devices. Cortical speech tracking was assessed through linear backward and forward models that related the EEG data to the speech envelope. For the CI users, electrical artifacts stemming from the implant were addressed through a bespoke method for artifact rejection. We found that the HA group exhibited cortical tracking and attentional modulation that were largely comparable to those of the TH group. CI users also showed successful cortical tracking. However, they displayed a profound deficit in attentional modulation, seen in the significantly poorer neural segregation of the attended vs. the ignored speech streams. These results shed light on a neurobiological mechanism for speech-in-noise comprehension and have implications for neurofeedback in hearing devices.

neuroscience↗