Biology subjects

Delorme, A.

Publications and source records attributed to Delorme, A..

12 recordsLinked to original sources

bioRxiv2026

EMG-BIDS: an extension to the Brain Imaging Data Structure for electromyography

Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of use, no widely adopted standard exists for organizing and sharing EMG data, limiting reusability and large-scale data aggregation. We present EMG-BIDS, an extension to the Brain Imaging Data Structure (BIDS) that standardizes the organization of EMG recordings. EMG-BIDS addresses challenges unique to EMG, including diverse electrode types (surface or intramuscular, single channel to high-density arrays), heterogeneous electrode placements across anatomical locations, montages (e.g., monopolar or bipolar sensor designs), and the critical need for transparent documentation of sensor positioning. The specification introduces hierarchical coordinate systems that link local electrode grids to anatomical landmarks, enabling precise and reproducible placement documentation. EMG-BIDS is now part of BIDS as of version 1.11.0, supported by existing tools, including MNE-BIDS and EEGLAB. We demonstrate the specification through public datasets, including high-density surface EMG recordings. EMG-BIDS provides the foundation for FAIR (Findable, Accessible, Interoperable, Reusable) EMG data sharing, enabling meta-analyses, multi-site studies, and machine learning applications that require standardized, well-documented datasets.

neuroscience↗

bioRxiv2024

HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset

The Child Mind Institute (CMI) Healthy Brain Network (HBN) project has recorded phenotypic, behavioral, and neuroimaging data from [~]5,000 children and young adults between the ages of 5 and 21. Here, we present HBN-EEG, the "analysis-ready" data from its high-density (128-channel) electroencephalographic (EEG) recording sessions formatted as Brain Imaging Data Structure (BIDS) datasets. HBN-EEG also includes behavioral and task-condition events annotated using Hierarchical Event Descriptors (HED), making the datasets analysis-ready for many purposes without forensic search for unreported details. We also ensured data consistency and event integrity and marked inconsistencies. HBN-EEG sessions include six tasks, three with no participant behavioral input (passive tasks) and three including button press responses following task instructions (active tasks). Openly available participant information includes age, gender, and four psychopathology dimensions (internalizing, externalizing, attention, and p-factor) derived from a bifactor model of questionnaire data. Currently, HBN-EEG data from more than 2,600 participants is freely available on NEMAR (nemar.org) and OpenNeuro in the form of nine dataset releases, with further dataset releases to follow.

neuroscience↗

bioRxiv2024

Identifying HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-minute) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincare SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearmans correlations, and corrections for multiple comparisons. Only 8 files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

physiology↗

bioRxiv2024

The Lab Streaming Layer for Synchronized Multimodal Recording

Accurately recording the interactions of humans or other organisms with their environment and other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) framework offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common local area netowrk (LAN). Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features can ensure continuous, millisecond-precise data recording, even in the face of interruptions. In this paper, we present an overview of LSL architecture, core features, and performance in common experimental contexts. We also highlight practical considerations and known pitfalls when using LSL, including the need to take into account input device throughput delays that LSL cannot itself measure or correct. The LSL ecosystem has grown to support over 150 data acquisition device classes and to establish interoperability between client software written in several programming languages including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording, now supported by a wide range of software packages including major stimulus presentation tools, real-time analysis envirnoments, and brain-computer interface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios including interactive art installations, stage performances, and commercial products. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes occurring within and across multiple data streams on a common timeline.

neuroscience↗

bioRxiv2023

EEG anticipatory activity depends on sensory modality

Perceptual decision-making is a combination of sensory information and prior beliefs. In order to perform actions in a timely fashion, it is necessary to anticipate the timing at which events occur, but also what event is more likely than the other. While EEG signatures of anticipation have been identified it is less clear whether classifiers trained on informative (cued) trials generalize to uncued trials and whether such decoded templates predict trial-by-trial shifts in decision strategy (e.g., drift-rate or starting-point changes in a Diffusion Decision Model). This study aimed to determine whether human participants anticipated a visual or auditory stimulus at the single-trial level in both cued and uncued trials. We found that pre-stimulus brain activity contains information about the expected upcoming stimulus and that this information can be successfully extracted from single-trial brain activity. Behavioral analyses revealed a connection between correct anticipation and shifts in decision strategy, while also validating the classification of uncued trials. Importantly, the classification of uncued trials confirms that expectations build even in the absence of triggers. These findings highlight the presence of single-trial, stimulus-specific neural signatures of anticipation, offering new insights into trial-to-trial variability in decision-making and advancing our understanding of cognitive processes. HighlightsO_LIDistributed low-frequency pre-stimulus EEG encodes modality-specific expectations. C_LIO_LIAnticipatory neural states are evoked even in the absence of explicit cues. C_LIO_LIDecoded anticipation predict response speed and accuracy. C_LI

neuroscience↗

bioRxiv2023

BrainBeats: an open-source EEGLAB plugin to jointly analyze EEG and cardiovascular (ECG/PPG) signals

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets and increasing reproducibility. A further void exists in standardized quantitative EEG (qEEG) and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) qEEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for performing these three methods on an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs via the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible. SUMMARYThe BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It offers three main protocols: heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. It should aid researchers and clinicians in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

neuroscience↗

bioRxiv2022

EEG is better left alone

Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We compared optimized pipelines for preprocessing EEG data maximizing ERP significance using the leading open-source EEG software: EEGLAB, FieldTrip, MNE, and Brainstorm. Only one pipeline performed significantly better than high-pass filtering the data.

neuroscience↗

bioRxiv2022

Identifying best practices for detecting inter-regional functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipeline that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis EEG data recorded during motor imagery.

neuroscience↗

bioRxiv2022

nl-DDM: a non-linear drift-diffusion model accounting for the dynamics of single-trial perceptual decisions

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

neuroscience↗

bioRxiv2021

Validating the wearable MUSE headset for EEG spectral analysis and Frontal Alpha Asymmetry

EEG power spectral density (PSD), the individual alpha frequency (IAF) and the frontal alpha asymmetry (FAA) are all EEG spectral measures that have been widely used to evaluate cognitive and attentional processes in experimental and clinical settings, and that can be used for real-world applications (e.g., remote EEG monitoring, brain-computer interfaces, neurofeedback, neuromodulation, etc.). Potential applications remain limited by the high cost, low mobility, and long preparation times associated with high-density EEG recording systems. Low-density wearable systems address these issues and can increase access to larger and diversified samples. The present study tested whether a low-cost, 4-channel wearable EEG system (the MUSE) could be used to quickly measure continuous EEG data, yielding similar frequency components compared to research a grade EEG system (the 64-channel BIOSEMI Active Two). We compare the spectral measures from MUSE EEG data referenced to mastoids to those from BIOSEMI EEG data with two different references for validation. A minimal amount of data was deliberately collected to test the feasibility for real-world applications (EEG setup and data collection being completed in under 5 min). We show that the MUSE can be used to examine power spectral density (PSD) in all frequency bands, the individual alpha frequency (IAF; i.e., peak alpha frequency and alpha center of gravity), and frontal alpha asymmetry. Furthermore, we observed satisfying internal consistency reliability in alpha power and asymmetry measures recorded with the MUSE. Estimating asymmetry on PAF and CoG frequencies did not yield significant advantages relative to the traditional method (whole alpha band). These findings should advance human neurophysiological monitoring using wearable neurotechnologies in large participant samples and increase the feasibility of their implementation in real-world settings.

neuroscience↗

bioRxiv2021

Capturing the nature of events and event context using Hierarchical Event Descriptors (HED)

Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities such as fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.org) combines robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper considers the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson made available by its authors as a Brain Imaging Data Structure (BIDS) dataset (bids.neuroimaging.io). We propose a set of best practices and guidelines for event annotation integrated in a natural way into the BIDS metadata file architecture, examine the impact of event design decisions, and provide a working example of organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using HED can document events occurring during neuroimaging experiments as well as their interrelationships, providing machine-actionable annotation enabling automated within- and across-experiment analysis and comparisons. We discuss the evolution of HED software tools and have made an accompanying HED-annotated BIDS-formatted edition of the MEEG data of the Wakeman and Henson dataset (openneuro.org, ds003645).

neuroscience↗

bioRxiv2020

Everyone Can Learn to Meditate: Characterizing a Personalized Connectomic State Space among Meditation Groups

Alterations in brain connectivity has been shown for many disease states and groups of people from different levels of cognitive training. To study dynamic functional connectivity, we propose a method for a personalized connectomic state space called Thought Chart. Experienced meditators are an interesting group of healthy subjects for brain connectivity analyses due to their demonstrated differences in resting state dynamics, and altered brain connectivity has been implicated as a potential factor in several psychiatric disorders. Three distinct techniques of meditation are explored: Isha Yoga, Himalayan Yoga, and Vipassana, as well as a meditation-naive group of individuals. All individuals participated in a breath awareness task, an autobiographical thinking task, and one of three different meditation practices according to their expertise, while being recorded by a 64-electrode electroencephalogram (EEG). The functional brain connectivity was estimated using weighted phase lag index (WPLI) and the connectivity dynamics were investigated using a within-individual formulation of Thought Chart, a previously proposed dimensionality reduction method which utilizes manifold learning to map out a state space of functional connectivity. Results showed that the two meditation tasks (breath awareness task and own form of meditation) in all groups were found to have consistently different functional connectivity patterns relative to those of the instructed mind-wandering (IMW) tasks in each individual, as measured using the Hausdorff distance in the state space. The specific meditation state was found to be most similar to the breath awareness state in all groups, as expected in these meditation traditions which all incorporate breath awareness training in their practice trajectory. The difference in connectivity was found to not be solely driven by specific frequency bands. These results demonstrate that the within-individual form of Thought Chart consistently and reliably separates similar tasks among healthy meditators and non-meditators during resting state-like EEG recordings. Unexpectedly, we found the dissimilarity between breath awareness/meditation and IMW, measured via Hausdorff distance, regardless of meditation experience or tradition, with no significant group differences.

neuroscience↗