Biology subjects

Bertrand, A.

Publications and source records attributed to Bertrand, A..

At least 19 recordsLinked to original sources

bioRxiv2026

When feedback backfires: investigating neurofeedback effects in a closed-loop auditory attention decoding paradigm

Selective auditory attention decoding (AAD) enables tracking which of multiple concurrent speakers a listener attends to and is a key building block for neuro-steered hearing devices. While AAD integrated in a closed-loop system with real-time neurofeedback (NFB) is hypothesized to improve decoding through neural adaptation and error-correction behaviour, the short-term behavioral and algorithmic impact of such a bilateral human-machine interaction remains poorly understood. Here we evaluated the effects of NFB on AAD accuracy and user experience in a single-session AAD paradigm with online NFB involving nineteen participants. They performed a selective listening task with enforced attention switches across four conditions: open-loop (OL), closed-loop with auditory gain feedback (CLA), closed-loop with visual feedback (CLV), and a condition with pseudo-auditory gain control (psCLA) decoupled from the participants individual neural activity. AAD was performed online using both subject-specific and subject-independent linear decoders on 5 s sliding windows, followed by Hidden Markov Model post-processing. Online analysis showed comparable decoding performance across all conditions. However, offline posthoc analysis using subject-independent decoders revealed that AAD accuracy in the CLA condition was significantly lower than in the OL baseline. Subjectively, participants reported that CLA was significantly more distracting and required higher switching effort. Crucially, a causal analysis of the psCLA condition found no robust evidence that higher audio gains inherently improve decoding accuracy. Our results demonstrate that within a single-session paradigm with rapidly varying feedback cues, auditory neurofeedback may degrade AAD performance by increasing cognitive load and distraction. These findings suggest that suboptimal feedback can impede rather than facilitate learning. We conclude that more accurate and stable decoders and longitudinal, multi-session training protocols are likely essential prerequisites for achieving beneficial neurofeedback effects in closed-loop auditory attention systems.

neuroscience↗

bioRxiv2026

Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposing

Sepsis is a life-threatening dysregulated response to infection, the heterogeneity of which precludes effective targeted therapies. To address this, we created a transcriptomic atlas of publicly available adult sepsis data, on which we performed molecular subtyping and identified potential subtype-specific drug repurposing opportunities. In total, we harmonized data from 3,713 samples across 28 datasets, of which 2,251 were from sepsis patients. Using this data, we identified four molecular subtypes of sepsis (C1 - C4) by clustering the sepsis samples based on expression differences in immune-and lipid-related genes. We next identified gene signatures unique to each molecular subtype. Pathway analysis of these signatures revealed patterns of immune exhaustion and metabolic dysregulation in C1, suggesting potential benefit from corticosteroid treatment. C2 had the youngest patient population and the lowest mortality, and C2 expression patterns were often anti-correlated with those of C1. C3 was enriched for inflammatory and cellular stress pathways, while the highest mortality subtype, C4, showed evidence of immunosuppression and metabolic reprogramming. Gene and pathway-level analysis of our molecular subtypes statistically correlated with results from analysis of 28-day mortality, with the best (C2) and worst subtypes (C4) exhibiting similar molecular dysregulation as survivors and non-survivors, respectively. For each subtype, we then evaluated potential targeted therapies. Using a large-scale pharmacogenomics database, we identified drugs targeting the subtype gene signatures and assessed the potential clinical impacts of these drugs. We identified several potential candidate therapies for each molecular subtype, including possible responsiveness to Methylene Blue therapy for patients from our highest mortality subtype, C4. Notably, our drug repurposing analysis revealed a significant representation of anti-inflammatory monoclonal antibody therapies across molecular subtypes. The anti-correlated signatures in C1 and C2 suggest that monoclonal antibody therapies may not be effective for patients in both subtypes, which may explain why prior clinical trials have been unsuccessful. Altogether, our detailed molecular subtyping and analysis identify potential drug targets within each molecular subtype, with implications for future precision medicine for sepsis.

bioinformatics↗

bioRxiv2026

Selective Auditory Attention Decoding with a Two-Node Wireless EEG Sensor Network

Selective auditory attention decoding (sAAD) enables neuro-steered hearing devices by identifying the attended speaker in a multi-speaker environment from neural activity recorded with electroencephalography (EEG). Despite algorithmic progress, practical deployment remains constrained by a lack of wearable, unobtrusive, and fully wireless EEG acquisition solutions. Therefore, this work aims to evaluate whether reliable sAAD can be achieved under realistic hardware constraints imposed by using a wireless EEG sensor network (WESN) consisting of miniaturized, galvanically isolated EEG sensor nodes. Here, we use such a WESN consisting of two synchronized, compact around-ear EEG sensor nodes worn bilaterally. Each node provides four local EEG channels derived from five pre-gelled electrodes, including a local reference. Sample-wise wireless synchronization of data from both nodes enables joint processing as an eight-channel EEG. On a newly recorded dataset acquired with this setup, correlation-based stimulus decoding achieves an average sAAD accuracy of 69.24% on 60s decision windows, comparable to wired around-ear EEG systems that measure long-distance scalp potentials. Hidden Markov model-based post-processing further improves to a steady-state accuracy of 77.17% with an average simulated attention switch detection time of 32.79s. Combining sensor nodes at both ears outperforms single-ear configurations, primarily by providing redundancy that increases robustness rather than by exploiting complementary spatial information. Finally, we show that a fixed bipolar configuration using four electrodes per ear, yielding three channels, suffices to maintain performance. These results demonstrate the practical feasibility of sAAD using a fully wireless, galvanically isolated around-ear WESN and establish a realistic performance benchmark under practical hardware constraints.

neuroscience↗

bioRxiv2025

EEG-based Decoding of Auditory Attention to Conversations with Turn-taking Speakers

ObjectivesAuditory attention decoding (AAD) refers to the process of identifying which sound source a listener is attending to, based on neural recordings, such as electroencephalography (EEG). Most AAD studies use a competing speaker paradigm where two continuously active speech signals are simultaneously presented, in which the participant is instructed to attend to one speaker and ignore the other speaker. However, such a competing two-speaker scenario is uncommon in real life, as speakers typically take turns rather than speaking simultaneously. In this paper, we argue that decoding attention to conversations (rather than individual speakers) is a more relevant paradigm for testing AAD algorithms. In such a conversation-tracking paradigm, the AAD algorithm focusses on switching between entire conversations, resulting in less frequent attention shifts (ignoring turn-taking within conversations), thereby allowing for more relaxed constraints on the decision time. DesignTo test AAD performance in such a conversation-tracking paradigm, we simulated a challenging restaurant scenario with three simultaneous two-speaker conversations, which were podcasts presented in front of the listener and in the back left and back right of the room. We conducted an EEG experiment on 20 normal-hearing participants to compare the performance of AAD in the commonly used competing speaker paradigm with two speakers versus the conversation tracking paradigm with 2 or 3 conversations, each containing two turn-taking speakers. ResultsWe found that AAD, using stimulus decoding, worked well under all experimental conditions, and that the accuracy was not influenced by the direction of attention, the proximity to the target conversation, or the presence of within-trial attention switches (versus a condition with sustained attention). Given the challenging scenario, we probed for the participants listening experience and found a correlation between the neural decoding performance and the perceived listening effort and self-reported speech intelligibility. To gain insight into the speech intelligibility of the participants in our setup, they performed a speech-in-noise test (Flemish matrix sentence test), but we did not find a correlation between the speech intelligibility performance and the AAD performance.

neuroscience↗

bioRxiv2025

Effects of speaker and listener sex on auditory attention decoding performance

ObjectivesAuditory Attention Decoding (AAD) is a technique utilizing brain signals to decode on which sound the listener focuses the attention. In most current studies, the effect of type of speech materials used and sex of the listener is not considered. We investigated the effect on AAD performance of factors related to the speaker (such as the sex of the speaker, background noise level, and same versus mixed-sex conditions) and the listener (sex of the listener). DesignForty-two young adults with normal hearing participated in the study. They listened to 2 competing speakers and were instructed to attend to one speaker and ignore the other speaker, whilst electroencephalography (EEG) and electrooculography (EOG) were measured. Background noise was introduced in half of the conditions. AAD performance was compared across eight experimental conditions. ResultsA significant main effect of speaker sex was found: A male target and/or male masker speaker resulted in higher AAD performance compared to a female speaker with a higher fundamental frequency. These effects were found to be small and therefore likely clinically irrelevant. ConclusionWhile no substantial effects were found on the factors investigated in this study, including diverse and realistic training scenarios remains a valuable approach to prevent potential influences from other factors.

neuroscience↗

bioRxiv2025

Advancing EEG Biosensing: Novel Scalable Production of Platinum-Silicon Microneedles

This study presents the development and analysis of a silicon microneedle electrode array fabricated using a streamlined, two-step approach for enhanced bio-signal recording applications. Numerical simulations with COMSOL Multiphysics were employed to optimize the microneedle array design, focusing on mechanical stability and minimizing skin-electrode impedance. Structural analysis identified needle lengths between 500 m and 700 m as optimal for mechanical robustness, with critical load factors indicating a high resistance to buckling under applied forces. Optimal needle spacing was determined to prevent inter-needle interference and ensure effective skin penetration. Proof-of-concept electroencephalography testing confirmed that the microneedles achieved reliable bio-signal recording, comparable to traditional wet electrodes, while offering advantages such as reduced preparation time and improved user comfort. This work establishes a simple and cleanroom-compatible, scalable method for fabricating platinum-coated silicon microneedle arrays, advancing the development of wearable, user-friendly biopotential monitoring devices.

bioengineering↗

bioRxiv2025

T-World: A highly general computational model of a human ventricular myocyte

Cardiovascular disease is the leading cause of death, demanding new tools to improve mechanistic understanding and overcome limitations of stem cell and animal-based research. We introduce T-World, a highly general virtual model of human ventricular cardiomyocyte suitable for multiscale studies. T-World shows comprehensive agreement with human physiology, from electrical activation to contraction, and is the first to replicate all key cellular mechanisms driving life-threatening arrhythmias. Extensively validated on unseen data, it demonstrates strong predictivity across applications and scales. Using T-World we revealed a likely sex-specific arrhythmia risk in females related to restitution properties, identified arrhythmia drivers in type 2 diabetes, and describe unexpected pro-arrhythmic role of NaV1.8 in heart failure. T-World demonstrates strong performance in predicting drug-induced arrhythmia risk and opens new opportunities for predicting and explaining drug efficacy, demonstrated by unpicking effects of mexiletine in Long QT syndrome 2. T-World is available as open-source code and an online app.

cell biology↗

bioRxiv2025

Synchronized EEG with two galvanically-separated miniature wireless behind-the-ear EEG sensors

We present a wireless EEG sensor network consisting of two miniature, wireless, behind-the-ear sensor nodes with a size of 2 cm x 3 cm, each containing a 4-channel EEG amplifier and a wireless radio. Each sensor operates independently, each having its own sampling clock, wireless radio, and local reference electrode, with full electrical isolation from the other. The absence of a wire between the two nodes enhances discreetness and flexibility in deployment, improves miniaturization potential, and reduces wire artifacts. A third identical node acts as a USB dongle, which receives and synchronizes the data from the two behind-the-ear nodes. The latter allows to process the 2 x 4 channel EEG as if all 8 channels are sampled synchronously, allowing the use of signal processing algorithms that exploit inter-channel correlations. To demonstrate this synchronized processing, we recorded auditory steady-state responses (ASSRs) at both ears and processed them with data-driven multi-channel filters to optimize the ASSR signal-to-noise ratio, demonstrating a more reliable ASSR detection compared to a single-ear setup.

bioengineering↗

bioRxiv2025

Temporal variation in the acoustic dynamic range is a confounding factor in EEG-based tracking of absolute auditory attention to speech

Many studies have demonstrated that auditory attention to natural speech can be decoded from EEG data. However, most studies focus on selective auditory attention decoding (sAAD) with competing speakers, while the dynamics of absolute auditory attention decoding (aAAD) to a single target remains underexplored. The goal of aAAD is to measure the degree of attention to a single speaker, and it has applications for objective measurements of attention in psychological and educational contexts. To investigate this aAAD paradigm, we designed an experiment where subjects listened to a video lecture under varying attentive conditions. We trained neural decoders to reconstruct the speech envelope from EEG in the baseline attentive condition and use the correlation coefficient between the decoded and real speech envelope as a metric for attention to the speech. Our analysis shows that the envelope standard deviation (SD) of the speech envelope in the 1-4 Hz band strongly correlates with this metric across different segments of the speech stimulus. However, this correlation weakens in the 0.1-4 Hz band, where the degree of separation between the attentive and inattentive state becomes more pronounced. This highlights the unique contribution of the 0.1-1 Hz range, which enhances the distinction of attentional states and remains less affected by confounding factors such as the time-varying dynamic range of the speech envelope.

neuroscience↗

bioRxiv2025

A wireless, scalable and modular EEG sensor network platform for unobtrusive brain recordings

This paper introduces a modular sensing platform for wearable electroencephalography (EEG) recordings. The platform is conceived as a wireless EEG sensor network (WESN), consisting of multiple miniaturized, wireless EEG sensor nodes that synchronously collect EEG data from different scalp locations. As there are no wires between the different sensors, the platform provides maximal flexibility and discreetness, combined with a reduced sensitivity to motion artefacts or electro-magnetic interference. By removing the driven right leg (DRL) electrode and reducing the within-node electrode spacing to 3cm, we obtain a compact design while maintaining a high signal integrity. The WESN system was validated through a series of experiments: achieving synchronization of EEG data transmission across multiple sensor nodes and the detection of actual neural responses in EEG experiments. These results demonstrate the effectiveness and robustness of the proposed WESN platform, establishing it as a promising research platform for scalable, flexible, and discreet multi-channel EEG monitoring in ambulatory settings.

bioengineering↗

bioRxiv2023

'Are you even listening?' - EEG-based detection of absolute auditory attention to natural speech with application to neuro-steered hearing devices

ObjectiveIn this study, we use electroencephalography (EEG) recordings to determine whether a subject is actively listening to a presented speech stimulus. More precisely, we aim to discriminate between an active listening condition, and a distractor condition where subjects focus on an unrelated distractor task while being exposed to a speech stimulus. We refer to this task as absolute auditory attention decoding. ApproachWe re-use an existing EEG dataset where the subjects watch a silent movie as a distractor condition, and introduce a new dataset with two distractor conditions (silently reading a text and performing arithmetic exercises). We focus on two EEG features, namely neural envelope tracking (NET) and spectral entropy (SE). Additionally, we investigate whether the detection of such an active listening condition can be combined with a selective auditory attention decoding task, where the goal is to decide to which of multiple competing speakers the subject is attending. The latter is a key task in so-called neuro-steered hearing devices that aim to suppress unattended audio, while preserving the attended speaker. Main resultsContrary to a previous hypothesis of higher SE being related with actively listening rather than passively listening (without any distractors), we find significantly lower SE in the active listening condition compared to the distractor conditions. Nevertheless, the NET is consistently significantly higher when actively listening. Similarly, we show that the accuracy of a selective auditory attention decoding task improves when evaluating the accuracy only on the highest NET segments. However, the reverse is observed when evaluating the accuracy only on the lowest SE segments. SignificanceWe conclude that the NET is more reliable for decoding absolute auditory attention as it is consistently higher when actively listening, whereas the relation of the SE between actively and passively listening seems to depend on the nature of the distractor.

neuroscience↗

bioRxiv2023

Identifying Temporal Correlations Between Natural Single-shot Videos and EEG Signals

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSElectroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored. ApproachWe collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features. Main ResultsWe demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch (MM) task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects. SignificanceThis work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.

bioengineering↗

bioRxiv2023

Neuronal tuning and population representations of shape and category in human visual cortex.

Object recognition and categorization are essential cognitive processes which engage considerable neural resources in the human ventral visual stream. However, the tuning properties of human ventral stream neurons for object shape and category are virtually unknown. We performed the first large-scale recordings of spiking activity in human Lateral Occipital Complex in response to stimuli in which the shape dimension was dissociated from the category dimension. Consistent with studies in nonhuman primates, the neuronal representations were primarily shape-based, although we also observed category-like encoding for images of animals. Surprisingly, linear decoders could reliably classify stimulus category even in data sets that were entirely shape-based. In addition, many tuning curves showed an interaction between shape and category tuning. These results represent the first detailed study on shape and category coding at the neuronal level in the human ventral visual stream, furnishing essential evidence that reconciles human imaging and macaque single-cell studies.

neuroscience↗

bioRxiv2023

EEG-based decoding of the spatial focus of auditory attention in a multi-talker audiovisual experiment using Common Spatial Patterns

ObjectiveSpatial auditory attention decoding (Sp-AAD) refers to the task of identifying the direction of the speaker to which a person is attending in a multi-talker setting, based on the listeners neural recordings, e.g., electroencephalography (EEG). The goal of this study is to thoroughly investigate potential biases when training such Sp-AAD decoders on EEG data, particularly eye-gaze biases and latent trial-dependent confounds, which may result in Sp-AAD models that decode eye-gaze or trial-specific fingerprints rather than spatial auditory attention. ApproachWe designed a two-speaker audiovisual Sp-AAD protocol in which the spatial auditory and visual attention were enforced to be either congruent or incongruent, and we recorded EEG data from sixteen participants undergoing several trials recorded at distinct timepoints. We trained a simple linear model for Sp-AAD based on common spatial patterns (CSP) filters in combination with either linear discriminant analysis (LDA) or k-means clustering, and evaluated them both across- and within-trial. Main resultsWe found that even a simple linear Sp-AAD model is susceptible to overfitting to confounding signal patterns such as eye-gaze and trial fingerprints (e.g., due to feature shifts across trials), resulting in artificially high decoding accuracies. Furthermore, we found that changes in the EEG signal statistics across trials deteriorate the trial generalization of the classifier, even when the latter is retrained on the test trial with an unsupervised algorithm. SignificanceCollectively, our findings confirm that there exist subtle biases and confounds that can strongly interfere with the decoding of spatial auditory attention from EEG. It is expected that more complicated non-linear models based on deep neural networks, which are often used for Sp-AAD, are even more vulnerable to such biases. Future work should perform experiments and model evaluations that avoid and/or control for such biases in Sp-AAD tasks.

neuroscience↗

bioRxiv2022

Stable population structure in Europe since the Iron Age, despite high mobility

Ancient DNA research in the past decade has revealed that European population structure changed dramatically in the prehistoric period (14,000-3,000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed from the historical period onward (3,000 YBP - present). To address this, we collected whole genomes from 204 individuals from Europe and the Mediterranean, many of which are the first historical period genomes from their region (e.g. Armenia and France). We found that most regions show remarkable inter-individual heterogeneity. At least 7% of historical individuals carry ancestry uncommon in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring geography. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. We hypothesize that this phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empires mobilization of people for trade, labor, and military. This work highlights the utility of ancient DNA in elucidating finer scale human population dynamics in recent history.

genetics↗

bioRxiv2022

Time-adaptive Unsupervised Auditory Attention Decoding Using EEG-based Stimulus Reconstruction

The goal of auditory attention decoding (AAD) is to determine to which speaker out of multiple competing speakers a listener is attending based on the brain signals recorded via, e.g., electroencephalography (EEG). AAD algorithms are a fundamental building block of so-called neuro-steered hearing devices that would allow identifying the speaker that should be amplified based on the brain activity. A common approach is to train a subject-specific stimulus decoder that reconstructs the amplitude envelope of the attended speech signal. However, training this decoder requires a dedicated ground-truth EEG recording of the subject under test, during which the attended speaker is known. Furthermore, this decoder remains fixed during operation and can thus not adapt to changing conditions and situations. Therefore, we propose an online time-adaptive unsupervised stimulus reconstruction method that continuously and automatically adapts over time when new EEG and audio data are streaming in. The adaptive decoder does not require ground-truth attention labels obtained from a training session with the end-user and instead can be initialized with a generic subject-independent decoder or even completely random values. We propose two different implementations: a sliding window and recursive implementation, which we extensively validate on three independent datasets based on multiple performance metrics. We show that the proposed time-adaptive unsupervised decoder outperforms a time-invariant supervised decoder, representing an important step toward practically applicable AAD algorithms for neuro-steered hearing devices.

neuroscience↗

bioRxiv2020

Optimal versus approximate channel selection methods for EEG decoding with application to topology-constrained neuro-sensor networks

Channel selection or electrode placement for neural decoding is a commonly encountered problem in electroencephalography (EEG). Since evaluating all possible channel combinations is usually infeasible, one usually has to settle for heuristic methods or convex approximations without optimality guarantees. To date, it remains unclear how large the gap is between the selection made by these approximate methods and the truly optimal selection. The goal of this paper is to quantify this optimality gap for several state-of-the-art channel selection methods in the context of least-squares based neural decoding. To this end, we reformulate the channel selection problem as a mixed-integer quadratic program (MIQP), which allows the use of efficient MIQP solvers to find the optimal channel combination in a feasible computation time for up to 100 candidate channels. As this reveals the exact solution to the combinatorial problem, it allows to quantify the performance losses when using state-of-the-art sub-optimal (yet faster) channel selection methods. In a context of auditory attention decoding, we find that a greedy channel selection based on the utility metric does not show a significant optimality gap compared to optimal channel selection, whereas other state-of-the-art greedy or l1-norm penalized methods do show a significant loss in performance. Furthermore, we demonstrate that the MIQP formulation also provides a natural way to incorporate topology constraints in the selection, e.g., for electrode placement in neuro-sensor networks with galvanic separation constraints. Furthermore, a combination of this utility-based greedy selection with an MIQP solver allows to perform a topology constrained electrode placement, even in large scale problems with more than 100 candidate positions.

bioengineering↗

bioRxiv2020

EEG-based decoding and recognition of imagined music

The authors have withdrawn their manuscript because they discovered an error in the analysis code after publication of the preprint, which turns out to have a major impact on the main results in the paper. The results on the imagination data become non-significant after correcting for the mistake. Significant results on the perception data are preserved, although the classification results are worse than what is reported. Therefore, the authors do not wish this work to be cited as a reference. If you have any questions, please contact the last author.

neuroscience↗