Evidence map›Paper›PMID 39026153›Full record

ArticleBMC bioinformatics2024

HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables.

Richard James Sugden, Ingrid Campbell, Viet-Linh Luke Pham-Kim-Nghiem-Phu, Randa Higazy, Eliza Dent, Kim Edelstein, Alberto Leon, Phedias Diamandis

Abstract read
In one paragraph

Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Richard James Sugden *Department of Medical Biophysics, University of Toronto, Toronto, ON, M5S 1A8, Canada.
Ingrid Campbell *Princess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada.
Viet-Linh Luke Pham-Kim-Nghiem-PhuPrincess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada.
Randa HigazyPrincess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada.
Eliza DentCognitive Science Program, McGill University, 845 Rue Sherbrooke O, Montréal, QC, H3A 0G4, Canada.
Kim EdelsteinDepartment of Supportive Care, Princess Margaret Cancer Centre, Toronto, ON, M5G 2C4, Canada.
Alberto LeonPrincess Margaret Cancer Center, University Health Network, 610 University Avenue, Toronto, ON, M5G 2C1, Canada.
Phedias DiamandisDepartment of Medical Biophysics, University of Toronto, Toronto, ON, M5S 1A8, Canada. p.diamandis@mail.utoronto.ca.

Funding

American Brain Tumor Association,United States #1051131
6 · The paper itself

Abstract

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC's implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants' homes. Our results demonstrate HEROIC's capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

Indexed as

BrainElectroencephalographySoftwareWearable Electronic DevicesEvoked PotentialsHumansMaleElectroencephalographyRemote medicineWearable devices

Identifiers

PMID39026153
PMCPMC11256487

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.