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Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines

Published: 04 October 2009 Publication History

Abstract

Because functional near-infrared spectroscopy (fNIRS) eases many of the restrictions of other brain sensors, it has potential to open up new possibilities for HCI research. From our experience using fNIRS technology for HCI, we identify several considerations and provide guidelines for using fNIRS in realistic HCI laboratory settings. We empirically examine whether typical human behavior (e.g. head and facial movement) or computer interaction (e.g. keyboard and mouse usage) interfere with brain measurement using fNIRS. Based on the results of our study, we establish which physical behaviors inherent in computer usage interfere with accurate fNIRS sensing of cognitive state information, which can be corrected in data analysis, and which are acceptable. With these findings, we hope to facilitate further adoption of fNIRS brain sensing technology in HCI research.

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    cover image ACM Conferences
    UIST '09: Proceedings of the 22nd annual ACM symposium on User interface software and technology
    October 2009
    278 pages
    ISBN:9781605587455
    DOI:10.1145/1622176
    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 ACM 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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    Published: 04 October 2009

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    Author Tags

    1. BCI
    2. brain-computer interface
    3. fNIRS
    4. functional near-infrared spectroscopy
    5. human cognition

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    • (2024)Crowdsourcing Affective Annotations Via fNIRS-BCIIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327391615:1(297-308)Online publication date: Jan-2024
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