Biology subjects

Ros, T.

Publications and source records attributed to Ros, T..

8 recordsLinked to original sources

bioRxiv2026

The Dry Truth: Hair-Wetting Improves Dry Electrode EEG Signal Quality

With the rapid advancement of clinical neuroscience and Brain-Computer Interfaces (BCIs), there is an increasing demand for convenient, user-friendly EEG recording methods suitable for diverse environments, including mobile and home-based settings. Traditional gel-based EEG systems, while reliable, are inconvenient and time-consuming, whereas dry electrode systems tend to suffer from elevated noise levels. In this study, we investigated a novel methodological manipulation aimed at enhancing dry electrode signal quality: wetting the hair directly with tap water to improve scalp electrode conductivity. To this end, we recruited 22 healthy participants and compared their resting-state (RS) EEG activity across three experimental conditions (dry, semi-dry, and gel) within a single-session design. Specifically, we analyzed electrode impedance, spectral power (SP), and functional connectivity (FC). Our results show that basic hair-wetting substantially improved mean electrode impedance by 75.7% relative to the dry condition, reduced the proportion of bad channels from approximately 40% to 15%, and increased spectral-power similarity with gel recordings by 7.7% during eyes-closed recordings, and improved FC similarity by approximately 56-70% across frequency bands. Although this method does not fully match the signal quality of traditional gel-based systems, it represents a promising compromise, enhancing EEG data quality under suboptimal conditions. This approach offers a practical and non-invasive means to improve EEG signal quality, ultimately expanding real-world applications of EEG.

neuroscience↗

bioRxiv2026

Volitional control of parieto-occipital alpha lateralization via neurofeedback does not influence auditory spatial attention

Auditory spatial attention, the ability to focus selectively on specific sounds while ignoring others, is crucial for everyday listening. Lateralized alpha oscillations over the parieto-occipital cortex have been proposed to act as a crossmodal attentional filter mechanism that allows to gate sensory processing by actively suppressing unattended locations. While prior evidence supports the functional role of this mechanism in visual attention, its role in auditory spatial attention remains debated. In this pre-registered EEG-neurofeedback study, participants were trained for 30 minutes to lateralize alpha power toward the left or right parieto-occipital cortex, while auditory probes from different spatial directions (-90{degrees}, -45{degrees}, 45{degrees}, and 90{degrees}) assessed whether online changes in alpha lateralization influenced auditory processing. Resting-state and task-based alpha lateralization, as well as gaze behavior were measured before and after training. Participants successfully modulated alpha lateralization in the trained direction during neurofeedback. However, shifting alpha lateralization did not cause changes in online auditory processing, as evidenced by the absence of asymmetric changes in auditory evoked responses to lateralized probe tones. Likewise, neurofeedback did not cause persistent changes in alpha lateralization during resting-state and task-based recordings after neurofeedback. Notably, neurofeedback training affected gaze behavior. Shifting alpha lateralization toward the left hemisphere during neurofeedback abolished a pre-existing rightward gaze bias in training responders, pointing to a dissociation between oculomotor and auditory attentional systems. These findings challenge the notion that parieto-occipital alpha lateralization serves as a universal crossmodal spatial gate, and raise important questions about the functional specificity of alpha-based neurofeedback interventions.

neuroscience↗

bioRxiv2025

Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

Current electroencephalogram (EEG) denoising methods struggle to remove the complex physiological and environmental artifacts typical of real-world settings, which both hinders the isolation of true neural activity and limits the technologys translational potential. We present the Generalized Eigenvalue De-Artifacting Instrument (GEDAI), a novel algorithm for denoising highly contaminated EEG. GEDAI employs leadfield filtering to selectively remove noise and artifacts that diverge from a theoretically defined EEG forward model. This approach offers unique advantages over existing solutions, including 1) denoising of highly corrupt recordings without "clean" reference data, 2) single-step correction of artifactual epochs and bad channels, 3) unsupervised detection of brain and noise components based on the signal and noise subspace alignment index (SENSAI). In ground-truth simulations with synthetic and empirical EEG contaminated with realistic artifacts (EOG, EMG, noise), GEDAI globally outperformed leading denoising techniques based on principal component analysis (ASR) and independent component analysis (IClabel, MARA), revealing large effect sizes in challenging scenarios with simultaneous artifact mixtures, low signal-to-noise ratio (-9 dB), and high temporal contamination (up to 100%). Its superior denoising also enhanced neurobehavioral predictions, yielding highest accuracies in ERP classification and brain fingerprinting. GEDAIs autonomy, computational speed and noise-resilience could find future applications in 1) real- world medical, mobile and dry electrode EEG recordings 2) magnetoenecephalography (MEG) denoising (given the shared M/EEG forward model), and 3) real-time brain-computer interfaces (BCIs). The Matlab code for GEDAI is available as an open-source EEGLAB plugin at https://github.com/neurotuning/GEDAI-master

neuroscience↗

bioRxiv2025

MEGaNorm: Normative Modeling of MEG Brain Oscillations Across the Human Lifespan

Normative modeling provides a principled framework for quantifying individual deviations from typical brain development and is increasingly used to study heterogeneity in neuropsychiatric conditions. While widely applied to structural phenotypes, functional normative models remain underdeveloped. Here, we introduce MEGaNorm, the first normative modeling framework for charting lifespan trajectories of resting-state magnetoencephalography (MEG) brain oscillations. Using a large, multi-site dataset comprising 1,846 individuals aged 6-88 and spanning three MEG systems, we model relative oscillatory power in canonical frequency bands using hierarchical Bayesian regression, accounting for age, sex, and site effects. To support interpretation at multiple scales, we introduce Neuro-Oscillo Charts, visual tools that summarize normative trajectories at the population level and quantify individual-level deviations, enabling personalized assessment of functional brain dynamics. Applying this framework to a Parkinsons disease cohort (n = 160), we show that normative deviation scores reveal disease-related abnormalities and uncover a continuum of patients in theta-beta deviation space. This work provides the first lifespan-encompassing normative reference for MEG oscillations, enabling population-level characterization and individualized benchmarking. All models and tools are openly available and designed for federated, continual adaptation as new data become available, offering a scalable resource for precision neuropsychiatry.

neuroscience↗

bioRxiv2023

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

ObjectiveServing as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the users brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The users BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results. ApproachTo systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline. Main resultsOur results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR. SignificanceTaken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the users BCI performance.

neuroscience↗

bioRxiv2020

Beyond broadband: towards a spectral decomposition of EEG microstates

Originally applied to alpha oscillations in the 1970s, MS analysis has since been used to decompose mainly broadband EEG signals (e.g. 1-40 Hz). We hypothesized that MS decomposition within separate, narrow frequency bands could provide more fine-grained information for capturing the spatio-temporal complexity of multichannel EEG. In this study using a large open-access dataset (n=203), we decomposed EEG recordings into 4 classical frequency bands (delta, theta, alpha, beta) in order to compare their individual MS segmentations using mutual information as well as traditional MS measures (e.g. mean duration, time coverage). Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, D). Interestingly however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures. For example, relative to broadband, alpha/beta band dynamics displayed greater time coverage of maps A & B, while map D was more prevalent in delta/theta bands. Moreover, by using a frequency-specificMS taxonomy (e.g. {theta}A, C), we were able to predict the eyes-open vs closed-behavioural state significantly better using alpha-band MS features compared with broadband ones (80% vs 73% accuracy). Overall, our findings demonstrate the value and validity of spectrally-specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.

neuroscience↗

bioRxiv2019

Linking alpha oscillations, attention and inhibitory control in adult ADHD with EEG neurofeedback

Abnormal patterns of electrical oscillatory activity have been repeatedly described in adult ADHD. In particular, the alpha rhythm (8-12 Hz), known to be modulated during attention, has previously been considered as candidate biomarker for ADHD. In the present study, we asked adult ADHD patients to self-regulate their own alpha rhythm using neurofeedback (NFB), in order to examine the modulation of alpha oscillations on attentional performance and brain plasticity. Twenty-five adult ADHD patients and 22 healthy controls underwent a 64-channel EEG-recording at resting-state and during a Go/NoGo task, before and after a 30 min-NFB session designed to reduce (desynchronize) the power of the alpha rhythm. Alpha power was compared across conditions and groups, and the effects of NFB were statistically assessed by comparing behavioral and EEG measures pre-to-post NFB. Firstly, we found that relative alpha power was attenuated in our ADHD cohort compared to control subjects at baseline and across experimental conditions, suggesting a signature of cortical hyper-activation. Both groups demonstrated a significant and targeted reduction of alpha power during NFB. Interestingly, we observed a post-NFB increase in resting-state alpha (i.e. rebound) in the ADHD group, which restored alpha power towards levels of the normal population. Importantly, the degree of post-NFB alpha normalisation during the Go/NoGo task correlated with individual improvements in motor inhibition (i.e. reduced commission errors and slower reaction times in NoGo trials) only in the ADHD group. Overall, our findings offer novel supporting evidence implicating alpha oscillations in inhibitory control, as well as their potential role in the homeostatic regulation of cortical excitatory/inhibitory balance.\n\nHighlightsO_LIResting alpha power is reduced in adult ADHD suggesting cortical hyper-activation\nC_LIO_LIAdult ADHD patients successfully reduce alpha power during neurofeedback\nC_LIO_LIA post-neurofeedback rebound normalizes alpha power in adult ADHD\nC_LIO_LIAlpha power rebound correlates with improvement of inhibitory control in adult ADHD\nC_LI

neuroscience↗

bioRxiv2019

Disruption of large-scale electrophysiological networks in stroke patients with visuospatial neglect

Stroke frequently produces attentional dysfunctions including symptoms of hemispatial neglect, which is characterized by a breakdown of awareness for the contralesional hemispace. Recent studies with functional MRI (fMRI) suggest that hemineglect patients display abnormal intra-and inter-hemispheric functional connectivity. However, since stroke is a vascular disorder and fMRI signals remain sensitive to non-neuronal (i.e. vascular) coupling, more direct demonstrations of neural network dysfunction in hemispatial neglect are warranted. Here, we utilize electroencephalogram (EEG) source imaging to uncover differences in resting-state network organization between patients with right-hemispheric stroke (N = 15) and age-matched, healthy controls (N = 27), and determine the relationship between hemineglect symptoms and brain network organization. We estimated intra-and inter-regional differences in cortical communication, by calculating the spectral power and amplitude envelope correlations (AEC) of narrow-band EEG oscillations. We firstly observed focal frequency-slowing within the right posterior cortical regions, reflected in relative delta/theta power increases and alpha/beta/gamma decreases. Secondly, nodes within the right temporal and parietal cortex consistently displayed anomalous intra- and inter- hemispheric coupling, stronger in delta and gamma bands, and weaker in theta, alpha, and beta bands. Finally, a significant association was observed between the severity of left-hemispace search deficits (e.g. cancellation test omissions) and reduced functional connectivity within the alpha and beta bands. In sum, our novel results validate the hypothesis of large-scale cortical network disruption following stroke, and reinforce the proposal that abnormal brain oscillations may be intimately involved in the pathophysiology of visuospatial neglect.

neuroscience↗