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Usability of EEG Systems: User Experience Study

Published: 29 June 2016 Publication History

Abstract

In recent years there was a change in EEG experimental designs - from simple behavior in the lab to complex behavior outside. That change required also an adjustment of EEG systems -- from being static and sensitive to mobile and noise-resistant. The rapid technological development has to balance performance (e.g. number of channels, low impedance contact) with usability (e.g. comfort for the participant, contact pressure, wet/dry electrodes) and mobility (e.g. wiring, weight). This has led to wide variety of designs which differ widely in properties. Here we compare 7 EEG systems with respect to the participant's user experience. Results demonstrate that from perspective of user experience of participants, mobile wet system (Cwet) had the highest score.

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cover image ACM Other conferences
PETRA '16: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments
June 2016
455 pages
ISBN:9781450343374
DOI:10.1145/2910674
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2016

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Author Tags

  1. EEG
  2. UX
  3. Usability
  4. comparability
  5. data quality
  6. interferences
  7. internal validity
  8. user experience

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  • Short-paper
  • Research
  • Refereed limited

Funding Sources

  • Cognition and Neuroergonomics / Collaborative Technology Alliance

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PETRA '16

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Cited By

View all
  • (2024)A Comparative Study of Scalograms for Human Activity Classification2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS59971.2024.10555697(1-5)Online publication date: 15-May-2024
  • (2021)Mobile Electroencephalography for Studying Neural Control of Human LocomotionFrontiers in Human Neuroscience10.3389/fnhum.2021.74901715Online publication date: 10-Nov-2021
  • (2021)Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up SurveyACM Transactions on Interactive Intelligent Systems10.1145/345795011:2(1-49)Online publication date: 21-Jul-2021
  • (2020)Cross-Modality Matching for Evaluating User Experience of Emerging Mobile EEG TechnologyIEEE Transactions on Human-Machine Systems10.1109/THMS.2020.2989380(1-8)Online publication date: 2020
  • (2020)A deep-learned skin sensor decoding the epicentral human motionsNature Communications10.1038/s41467-020-16040-y11:1Online publication date: 1-May-2020
  • (2019)User Experience of 7 Mobile Electroencephalography Devices: Comparative StudyJMIR mHealth and uHealth10.2196/144747:9(e14474)Online publication date: 3-Sep-2019
  • (2018)Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during MotionSensors10.3390/s1804107318:4(1073)Online publication date: 3-Apr-2018

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