Decoding Mental Workload in Virtual Environments: A fNIRS Study using an Immersive n-back Task

F. Putze; C. Herff; C. Tremmel; T. Schultz; D.J. Krusienski

doi:10.1109/EMBC.2019.8856386

Decoding Mental Workload in Virtual Environments: A fNIRS Study using an Immersive n-back Task

F. Putze^*, C. Herff, C. Tremmel, T. Schultz, D.J. Krusienski

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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Abstract

Virtual Reality (VR) has emerged as a novel paradigm for immersive applications in training, entertainment, rehabilitation, and other domains. In this paper, we investigate the automatic classification of mental workload from brain activity measured through functional near-infrared spectroscopy (fNIRS) in VR. We present results from a study which implements the established n-back task in an immersive visual scene, including physical interaction. Our results show that user workload can be detected from fNIRS signals in immersive VR tasks both person-dependently and -adaptively.

Original language	English
Title of host publication	2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Publisher	IEEE Xplore
Pages	3103-3106
Number of pages	4
ISBN (Print)	978-1-5386-1312-2
DOIs	https://doi.org/10.1109/EMBC.2019.8856386
Publication status	Published - 1 Jan 2019

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title = "Decoding Mental Workload in Virtual Environments: A fNIRS Study using an Immersive n-back Task",

abstract = "Virtual Reality (VR) has emerged as a novel paradigm for immersive applications in training, entertainment, rehabilitation, and other domains. In this paper, we investigate the automatic classification of mental workload from brain activity measured through functional near-infrared spectroscopy (fNIRS) in VR. We present results from a study which implements the established n-back task in an immersive visual scene, including physical interaction. Our results show that user workload can be detected from fNIRS signals in immersive VR tasks both person-dependently and -adaptively.",

author = "F. Putze and C. Herff and C. Tremmel and T. Schultz and D.J. Krusienski",

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publisher = "IEEE Xplore",

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Decoding Mental Workload in Virtual Environments: A fNIRS Study using an Immersive n-back Task. / Putze, F.; Herff, C.; Tremmel, C. et al.
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). IEEE Xplore, 2019. p. 3103-3106.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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AU - Krusienski, D.J.

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N2 - Virtual Reality (VR) has emerged as a novel paradigm for immersive applications in training, entertainment, rehabilitation, and other domains. In this paper, we investigate the automatic classification of mental workload from brain activity measured through functional near-infrared spectroscopy (fNIRS) in VR. We present results from a study which implements the established n-back task in an immersive visual scene, including physical interaction. Our results show that user workload can be detected from fNIRS signals in immersive VR tasks both person-dependently and -adaptively.

AB - Virtual Reality (VR) has emerged as a novel paradigm for immersive applications in training, entertainment, rehabilitation, and other domains. In this paper, we investigate the automatic classification of mental workload from brain activity measured through functional near-infrared spectroscopy (fNIRS) in VR. We present results from a study which implements the established n-back task in an immersive visual scene, including physical interaction. Our results show that user workload can be detected from fNIRS signals in immersive VR tasks both person-dependently and -adaptively.

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DO - 10.1109/EMBC.2019.8856386

M3 - Conference article in proceeding

SN - 978-1-5386-1312-2

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BT - 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

PB - IEEE Xplore

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Decoding Mental Workload in Virtual Environments: A fNIRS Study using an Immersive n-back Task

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