Biology subjects

Debener, S.

Publications and source records attributed to Debener, S..

7 recordsLinked to original sources

bioRxiv2026

Does Pre-Sensorimotor Brain Activity Modulate TMS-Evoked Potentials?

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) provides a direct measure of cortical responses to local brain perturbations and offers a powerful approach for investigating brain-state-dependent neural dynamics. However, it remains unclear to what extent ongoing sensorimotor brain activity influences TMS-evoked potentials (TEPs). To address this question, thirty healthy participants performed kinesthetic motor imagery of left- and right-index finger tapping while single-pulse TMS was applied over the left primary motor cortex. 64-channel EEG was recorded to characterize sensorimotor oscillatory activity and TEPs. Time-frequency analyses quantified event-related desynchronization (ERD) in the mu (8-12 Hz) and beta (13-30 Hz) frequency bands, while TEP amplitudes were examined within early latency windows (10-65 ms). Motor imagery induced robust bilateral mu- and beta-band ERD, confirming successful engagement of the sensorimotor network. However, ERD patterns were highly similar between left- and right-hand motor imagery, and neither frequentist nor bayesian analyses identified differences in TMS-evoked potentials between imagery conditions. Source reconstruction demonstrated rapid propagation of TMS-evoked activity from the stimulated motor cortex to distributed bilateral motor regions. Correlation analyses revealed that greater pre-stimulus beta-band ERD was associated with larger P60 amplitudes. Because the instructed motor imagery conditions did not produce distinguishable pre-stimulus brain states, an exploratory trial-by-trial analysis was subsequently performed to account for naturally occurring variability in sensorimotor lateralization to investigate the effect of variability in ERD on TRP. This analysis revealed an overall effect of trial-by-trial brain state on TEP amplitudes

neuroscience↗

bioRxiv2026

Neurofeedback enhances a neural signature of selective attention to speech in cocktail-party settings

Understanding speech in noisy situations is challenging and often fails due to attentional rather than sensory deficits. We report here that neurofeedback can enhance a neural signature of selective attention to speech in a cocktail-party setting. Neural responses to speech were quantified in participants electroencephalogram (EEG) while they attended to one of two audiobooks, presented simultaneously. After each 22s-segment of speech, the participants N1 component was extracted from the temporal response function (TRF). The N1 component is known to be sensitive to attention. During a [~]49-min training session, N1 amplitude values were displayed visually to participants so that they could learn to strengthen their neural responses to target speech and minimise their responses to distracting speech. During neurofeedback training, we found an enhanced N1 component in the response to target audiobooks that was specific to EEG channels used to provide feedback, and not present in a control group that received sham feedback. At right-lateralised fronto-central channels, enhanced N1 components correlated with improvements in a measure of speech comprehension (multiple-choice content questions). These results indicate that neural responses to speech can be regulated through neurofeedback and open up new possibilities to train attentional listening in populations struggling to understand speech in noise.

neuroscience↗

bioRxiv2025

Lost in a Large EEG Multiverse? Comparing Sampling Approaches for Representative Pipeline Selection

The multiplicity of defensible strategies for processing and analysing data has been implicated as a core contributor to the replicability crisis, creating uncertainty about the robustness of a result to variations in data processing choices. This issue is exacerbated where a large number of data processing pipelines are defensible, and where there is great heterogeneity in the pipelines applied in the literature, such as in processing and analysing electroencephalography (EEG) signals. In a multiverse analysis, equally defensible pipelines are computed and the robustness of the result to these variations is reported. However, a large number of defensible pipelines is sometimes infeasible to compute exhaustively, and researchers rely on sampling approaches. In these cases, pipelines are sampled from the full multiverse and the robustness is reported across these samples, assuming that they are representative for the entire multiverse. However, different sampling methods may yield different robustness results, introducing what we term multiverse sampling uncertainty. To illustrate, we computed a 528-pipeline multiverse analysis on EEG-recordings during an emotion classification task aiming to predict extraversion scores from the Late Positive Potential. We applied three sampling methods (random, stratified, and active learning) to sample 26 pipelines (5%), and evaluated the results in terms of the representativeness of the distribution of model fits to that of the full multiverse. Our results highlight variability in the representativeness of the distribution of model fits between samples. The active learning sample most closely represented the median model fit of the full multiverse. The need for representative pipeline sampling to mitigate bias in large multiverse analyses is discussed.

neuroscience↗

bioRxiv2024

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where complex processing is necessary to address motion artifacts. Specifically, we describe the preprocessing choices studies reported for analyzing the P3 event-related potential (ERP) during walking and standing. A systematic review of 258 studies of the P3 during walking, identified 27 studies meeting the inclusion criteria. Two independent coders extracted preprocessing choices reported in each study. Analysis of preprocessing choices revealed commonalities and differences, such as the widespread use of offline filters but limited application of line noise correction (3 of 27 studies). Notably, 59% of studies involved manual processing steps, and 56% omitted reporting critical parameters for at least one step. All studies employed unique preprocessing strategies. These findings align with stationary EEG preprocessing results, emphasizing the necessity for standardized reporting in mobile EEG research. We implemented an interactive visualization tool (Shiny app) to aid the exploration of the preprocessing landscape. The app allows users to structure the literature regarding different processing steps, enter planned processing methods, and compare them with the literature. The app could be utilized to examine how these choices impact P3 results and understand the robustness of various processing options. We hope to increase awareness regarding the potential influence of preprocessing decisions and advocate for comprehensive reporting standards to foster reproducibility in mobile EEG research.

neuroscience↗

bioRxiv2024

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The large number of different analytical choices researchers use may be partly responsible for the replication challenge in neuroimaging studies. For robustness analysis, knowledge of the full space of options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable options. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the options within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) distinct sequencing of steps, and 3) parameter tuning within steps. To facilitate access to the data, we developed a decision support app with high educational value called METEOR, which allows researchers to explore the space of choices as reference for well-informed robustness (multiverse) analysis. HighlightsO_LIData analysis variability in neuroimaging hinders replicability. C_LIO_LIAnalysis across multiple defensible options examines the robustness of results. C_LIO_LIWe conducted a systematic literature review to identify analytical options. C_LIO_LIWe identified 61 steps and 102 options in performing graph-fMRI analysis. C_LIO_LIInteractive visualization of these steps and options is available as a Shiny app. C_LI

neuroscience↗

bioRxiv2022

Early visual cortex tracks speech envelope in the absence of visual input

Neural entrainment to continuous speech is typically observed within the language network and can be modulated by both low-level acoustic features and high-level meaningful linguistic units (e.g., phonemes, phrases, and sentences). Recent evidence showed that visual cortex may entrain to speech envelope, however its putative role in the hierarchy of speech processing remains unknown. We tested blindfolded participants who listened to semantically meaningful or meaningless stories, either in quiet or embedded in multi-talker babble noise. Entrainment to speech was assessed with forward linear modeling of participants EEG activity. We investigated (1) low-level acoustic effects by contrasting neural tracking of speech presented in quiet or noise and (2) high-level linguistic effects by contrasting neural tracking to meaningful or meaningless stories. Results showed that envelope tracking was enhanced and delayed for speech embedded in noise compared to quiet. When semantic information was missing, entrainment to speech envelope was fastened and reduced. Source modeling revealed that envelope tracking engaged wide neural networks beyond the auditory cortex, including early visual cortex. Surprisingly, while no clear influence of semantic content was found, the magnitude of visual cortex entrainment was affected by low-level features. The decrease of sound SNR-level dampened visual cortex tracking, suggesting an active suppressing mechanism in challenging listening conditions. Altogether, these findings provide further evidence of a functional role of early visual cortex in the entrainment to continuous speech.

neuroscience↗

bioRxiv2021

Real-time audio processing of real-life soundscapes for EEG analysis: ERPs based on natural sound onsets

With smartphone-based mobile electroencephalography (EEG), we can investigate sound perception beyond the lab. To understand sound perception in the real world, we need to relate naturally occurring sounds to EEG data. For this, EEG and audio information need to be synchronized precisely, only then it is possible to capture fast and transient evoked neural responses and relate them to individual sounds. We have developed Android applications (AFEx and Record-a) that allow for the concurrent acquisition of EEG data and audio features, i.e., sound onsets, average signal power (RMS) and power spectral density (PSD) on smartphone. In this paper, we evaluate these apps by computing event-related potentials (ERPs) evoked by everyday sounds. One participant listened to piano notes (played live by a pianist) and to a home-office soundscape. Timing tests showed that the temporal precision of the system is very good. We calculated ERPs to sound onsets and observed the typical P1-N1-P2 complex of auditory processing. Furthermore, we show how to relate information on loudness (RMS) and spectra (PSD) to brain activity. In future studies, we can use this system to study sound processing in everyday life.

neuroscience↗