Literature DB >> 27894782

Transitioning EEG experiments away from the laboratory using a Raspberry Pi 2.

Jonathan W P Kuziek1, Axita Shienh1, Kyle E Mathewson2.   

Abstract

BACKGROUND: Electroencephalography (EEG) experiments are typically performed in controlled laboratory settings to minimise noise and produce reliable measurements. These controlled conditions also reduce the applicability of the obtained results to more varied environments and may limit their relevance to everyday situations. NEW
METHOD: Advances in computer portability may increase the mobility and applicability of EEG results while decreasing costs. In this experiment we show that stimulus presentation using a Raspberry Pi 2 computer provides a low cost, reliable alternative to a traditional desktop PC in the administration of EEG experimental tasks.
RESULTS: Significant and reliable MMN and P3 activity, typical event-related potentials (ERPs) associated with an auditory oddball paradigm, were measured while experiments were administered using the Raspberry Pi 2. While latency differences in ERP triggering were observed between systems, these differences reduced power only marginally, likely due to the reduced processing power of the Raspberry Pi 2. COMPARISON WITH EXISTING
METHOD: An auditory oddball task administered using the Raspberry Pi 2 produced similar ERPs to those derived from a desktop PC in a laboratory setting. Despite temporal differences and slight increases in trials needed for similar statistical power, the Raspberry Pi 2 can be used to design and present auditory experiments comparable to a PC.
RESULTS: Our results show that the Raspberry Pi 2 is a low cost alternative to the desktop PC when administering EEG experiments and, due to its small size and low power consumption, will enable mobile EEG experiments unconstrained by a traditional laboratory setting.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  EEG; ERP; Ecological; Hardware; Portable; Raspberry Pi; Software

Mesh:

Year:  2016        PMID: 27894782     DOI: 10.1016/j.jneumeth.2016.11.013

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  10 in total

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4.  Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research.

Authors:  Olave E Krigolson; Chad C Williams; Angela Norton; Cameron D Hassall; Francisco L Colino
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Authors:  Marcos L Pietto; Mathias Gatti; Federico Raimondo; Sebastián J Lipina; Juan E Kamienkowski
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Review 7.  Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges.

Authors:  Alex Lau-Zhu; Michael P H Lau; Gráinne McLoughlin
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8.  Recording mobile EEG in an outdoor environment reveals cognitive-motor interference dependent on movement complexity.

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Journal:  Sci Rep       Date:  2019-09-11       Impact factor: 4.379

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Authors:  Florentine M Barbey; Francesca R Farina; Alison R Buick; Lena Danyeli; John F Dyer; Md Nurul Islam; Marina Krylova; Brian Murphy; Hugh Nolan; Laura M Rueda-Delgado; Martin Walter; Robert Whelan
Journal:  Front Digit Health       Date:  2022-07-29

10.  Feasibility of Repeated Assessment of Cognitive Function in Older Adults Using a Wireless, Mobile, Dry-EEG Headset and Tablet-Based Games.

Authors:  Esther C McWilliams; Florentine M Barbey; John F Dyer; Md Nurul Islam; Bernadette McGuinness; Brian Murphy; Hugh Nolan; Peter Passmore; Laura M Rueda-Delgado; Alison R Buick
Journal:  Front Psychiatry       Date:  2021-06-25       Impact factor: 4.157

  10 in total

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