Wireless EEG: A survey of systems and studies

NeuroImage, Journal Year: 2022, Volume and Issue: 269, P. 119774 - 119774

Published: Dec. 22, 2022

The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention recent years, focusing mostly on hardware miniaturization. This led to varied landscape portable EEG devices with wireless capability, allowing them be used by relatively unconstrained users real-life conditions outside the laboratory. wide availability and relative affordability these provide low entry threshold for newcomers field research. large device variety at times opaque communication from their manufacturers, however, can make it difficult obtain an overview this landscape. Similarly, given breadth existing (wireless) knowledge research, challenging get started novel ideas. Therefore, paper first provides list 48 along number important-sometimes difficult-to-obtain-features characteristics enable side-by-side comparison, brief introduction each aspects how they may influence one's decision. Secondly, we have surveyed previous literature focused 110 high-impact journal publications making use EEG, which categorized application analyzed used, channels, sample size, participant mobility. Together, basis informed decision respect experimental precedents when considering new, At same time, background material commentary about pitfalls caveats regarding increasingly accessible line

Language: Английский

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brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Nature Methods, Journal Year: 2024, Volume and Issue: 21(5), P. 809 - 813

Published: April 11, 2024

Neuroscience is advancing standardization and tool development to support rigor transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable reusable) access. brainlife.io was developed democratize neuroimaging research. The platform provides standardization, management, visualization processing automatically tracks the provenance history of thousands objects. Here, described evaluated for validity, reliability, reproducibility, replicability scientific utility using four modalities 3,200 participants.

Language: Английский

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Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

World Psychiatry, Journal Year: 2024, Volume and Issue: 23(1), P. 26 - 51

Published: Jan. 12, 2024

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it faced challenges criticisms, most notably a lack clinical translation. This paper provides comprehensive review critical summary literature on functional neuroimaging, in particular magnetic resonance imaging (fMRI), We begin by reviewing research fMRI biomarkers schizophrenia high risk phase through historical lens, moving from case-control regional brain activation to global connectivity advanced analytical approaches, more recent machine learning algorithms identify predictive features. Findings studies negative symptoms as well neurocognitive social cognitive deficits are then reviewed. neural markers these may represent promising treatment targets Next, we summarize related antipsychotic medication, psychotherapy psychosocial interventions, neurostimulation, including response resistance, therapeutic mechanisms, targeting. also utility data-driven approaches dissect heterogeneity schizophrenia, beyond comparisons, methodological considerations advances, consortia precision fMRI. Lastly, limitations future directions field discussed. Our suggests that, order for be clinically useful care patients should address potentially actionable decisions that routine treatment, such which prescribed or whether given patient is likely have persistent impairment. The potential influenced must weighed against cost accessibility factors. Future evaluations prognostic consider health economics analysis.

Language: Английский

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Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging

Nature Methods, Journal Year: 2024, Volume and Issue: 21(5), P. 804 - 808

Published: Jan. 8, 2024

Language: Английский

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Reproducibility in Neuroimaging Analysis: Challenges and Solutions

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Journal Year: 2022, Volume and Issue: 8(8), P. 780 - 788

Published: Dec. 19, 2022

Language: Английский

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Good scientific practice in EEG and MEG research: Progress and perspectives

NeuroImage, Journal Year: 2022, Volume and Issue: 257, P. 119056 - 119056

Published: March 10, 2022

Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, guidelines that help scientists produce work is of the highest quality at any given time, efficiently share with community for further scrutiny or utilization. For experimental research using magneto- electroencephalography (MEEG), GSP includes specific standards technical competence, which are periodically updated adapted new findings. However, also needs be regularly revisited in a broader light. At LiveMEEG 2020 conference, reflection on was fostered included explicitly documented advances, but emphasized intangible GSP: general awareness personal, organizational, societal realities how they can influence MEEG research. This article provides an extensive report most contributions literature, additional aim synthesize ongoing cultural changes GSP. It first covers respect cognitive biases logical fallacies, pre-registration as tool avoid those other early pitfalls, number resources enable collaborative reproducible approach minimize misconceptions. Second, it data acquisition, analysis, reporting, sharing, including tools frameworks support work. Finally, considered light ethical implications resulting responsibility have engage challenges. Considering among things benefits peer review open access all stages, need coordinate larger international projects, complexity subject matter, today's prioritization fairness, privacy, environment, we find current tends favor collective cooperative work, reasons.

Language: Английский

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DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Sept. 11, 2023

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, analysis of EEG data time-consuming researcher-dependent. Therefore, we developed DISCOVER-EEG, an open fully automated pipeline that enables easy fast analysis, visualization resting state data. Data the Brain Imaging Structure (BIDS) standard are automatically preprocessed, physiologically meaningful features brain function (including oscillatory power, connectivity, network characteristics) extracted visualized using two open-source widely used Matlab toolboxes (EEGLAB FieldTrip). We tested large, openly available datasets containing recordings healthy participants patients with condition. Additionally, performed exploratory could inspire development biomarkers aging. Thus, DISCOVER-EEG facilitates aggregation, reuse, large datasets, promoting research function.

Language: Английский

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The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Science Bulletin, Journal Year: 2024, Volume and Issue: 69(10), P. 1536 - 1555

Published: March 6, 2024

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection subtle abnormalities and robust associations, fostering new research methods. Global collaborations imaging have furthered knowledge neurobiological foundations brain disorders aided imaging-based prediction for more targeted treatment. Large-scale magnetic resonance initiatives driving innovation analytics supporting generalizable psychiatric studies. We also emphasize significant role big understanding neural mechanisms early identification precise treatment However, challenges such as harmonization across different sites, privacy protection, effective sharing must be addressed. With proper governance science practices, we conclude with a projection how large-scale resources could revolutionize diagnosis, selection, outcome prediction, contributing to optimal health.

Language: Английский

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Using preregistration as a tool for transparent fNIRS study design

Neurophotonics, Journal Year: 2023, Volume and Issue: 10(02)

Published: March 8, 2023

The expansion of functional near-infrared spectroscopy (fNIRS) methodology and analysis tools gives rise to various design analytical decisions that researchers have make. Several recent efforts developed guidelines for preprocessing, analyzing, reporting practices. For the planning stage fNIRS studies, similar guidance is desirable. Study preregistration helps transparently document study protocols before conducting study, including materials, methods, analyses, thus, others verify, understand, reproduce a study. Preregistration can thus serve as useful tool transparent, careful, comprehensive design.We aim create guide on steps involved in studies provide template specified studies.The presented has strong focus specific requirements, associated provides examples based continuous-wave (CW) conducted humans. These can, however, be extended other types studies.On step-by-step basis, we walk user through key methodological analysis-related aspects central design. include items CW, task-based but also sections are general importance, an in-depth elaboration sample size planning.Our introduces these open science community, providing with overview specification recommendations planning. As such it used preregister or merely transparent

Language: Английский

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Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(3), P. e1011942 - e1011942

Published: March 18, 2024

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used the literature, and practitioners rely on benchmarks guidance selection of an appropriate choice their study. However, software ever-evolving field, can quickly become obsolete as techniques or implementations change. In this work, we present benchmark featuring range strategies, datasets evaluation metrics analyses, based popular fMRIprep software. The prototypes implementation reproducible framework, where provided Jupyter Book enables readers to reproduce modify figures Neurolibre preprint server (https://neurolibre.org/). We demonstrate how such be continuous research software, by comparing two versions fMRIprep. Most results were consistent with prior literature. Scrubbing, technique which excludes time points excessive motion, combined global signal regression, generally effective at noise removal. Scrubbing was effective, but incompatible statistical analyses requiring sampling brain signal, simpler strategy, using motion parameters, average activity select compartments, preferred. Importantly, found that certain behave inconsistently across and/or fMRIPrep, had different behavior than previously published benchmarks. This work will hopefully provide useful guidelines users community, highlight importance methods.

Language: Английский

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