BCI for physiological text annotation

Oswald Barral; Ilkka Kosunen; Tuukka Ruotsalo; Michiel M. Spapé; Manuel J A Eugster; Niklas Ravaja; Samuel Kaski; Giulio Jacucci

doi:10.1145/3038439.3038445

BCI for physiological text annotation

Oswald Barral, Ilkka Kosunen, Tuukka Ruotsalo, Michiel M. Spapé, Manuel J A Eugster, Niklas Ravaja, Samuel Kaski, Giulio Jacucci

University of Helsinki

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

1 Citation (Scopus)

Abstract

Automatic annotation of media content has become a critically important task for many digital services as the quantity of available online media content has grown exponentially. One approach is to annotate the content using the physiological responses of the media consumer. In the present paper, we reflect on three case studies that use brain signals for implicit text annotation to discuss the challenges faced when bringing passive brain-computer interfaces for physiological text annotation to the real world.

Original language	English
Title of host publication	BCIforReal 2017 - Proceedings of the 2017 ACM Workshop on An Application-Oriented Approach to BCI Out of the Laboratory, co-located with IUI 2017
Publisher	ACM
Pages	9-13
Number of pages	5
ISBN (Electronic)	978-1-4503-4901-7
DOIs	https://doi.org/10.1145/3038439.3038445
Publication status	Published - 13 Mar 2017
MoE publication type	A4 Conference publication
Event	International Conference on Intelligent User Interfaces - Limassol, Cyprus Duration: 13 Mar 2017 → 16 Mar 2017 Conference number: 22

Conference

Conference	International Conference on Intelligent User Interfaces
Abbreviated title	IUI
Country/Territory	Cyprus
City	Limassol
Period	13/03/2017 → 16/03/2017

Keywords

Passive BCI
Physiological computing
Physiological text annotation

Access to Document

10.1145/3038439.3038445

Cite this

@inproceedings{9e521a7838d54c3182d803ed8408e8c2,

title = "BCI for physiological text annotation",

abstract = "Automatic annotation of media content has become a critically important task for many digital services as the quantity of available online media content has grown exponentially. One approach is to annotate the content using the physiological responses of the media consumer. In the present paper, we reflect on three case studies that use brain signals for implicit text annotation to discuss the challenges faced when bringing passive brain-computer interfaces for physiological text annotation to the real world.",

keywords = "Passive BCI, Physiological computing, Physiological text annotation",

author = "Oswald Barral and Ilkka Kosunen and Tuukka Ruotsalo and Spap{\'e}, {Michiel M.} and Eugster, {Manuel J A} and Niklas Ravaja and Samuel Kaski and Giulio Jacucci",

year = "2017",

month = mar,

day = "13",

doi = "10.1145/3038439.3038445",

language = "English",

pages = "9--13",

booktitle = "BCIforReal 2017 - Proceedings of the 2017 ACM Workshop on An Application-Oriented Approach to BCI Out of the Laboratory, co-located with IUI 2017",

publisher = "ACM",

address = "United States",

note = "International Conference on Intelligent User Interfaces, IUI ; Conference date: 13-03-2017 Through 16-03-2017",

}

Barral, O, Kosunen, I, Ruotsalo, T, Spapé, MM, Eugster, MJA, Ravaja, N, Kaski, S & Jacucci, G 2017, BCI for physiological text annotation. in BCIforReal 2017 - Proceedings of the 2017 ACM Workshop on An Application-Oriented Approach to BCI Out of the Laboratory, co-located with IUI 2017. ACM, pp. 9-13, International Conference on Intelligent User Interfaces, Limassol, Cyprus, 13/03/2017. https://doi.org/10.1145/3038439.3038445

BCI for physiological text annotation. / Barral, Oswald; Kosunen, Ilkka; Ruotsalo, Tuukka et al.
BCIforReal 2017 - Proceedings of the 2017 ACM Workshop on An Application-Oriented Approach to BCI Out of the Laboratory, co-located with IUI 2017. ACM, 2017. p. 9-13.

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

TY - GEN

T1 - BCI for physiological text annotation

AU - Barral, Oswald

AU - Kosunen, Ilkka

AU - Ruotsalo, Tuukka

AU - Spapé, Michiel M.

AU - Eugster, Manuel J A

AU - Ravaja, Niklas

AU - Kaski, Samuel

AU - Jacucci, Giulio

N1 - Conference code: 22

PY - 2017/3/13

Y1 - 2017/3/13

N2 - Automatic annotation of media content has become a critically important task for many digital services as the quantity of available online media content has grown exponentially. One approach is to annotate the content using the physiological responses of the media consumer. In the present paper, we reflect on three case studies that use brain signals for implicit text annotation to discuss the challenges faced when bringing passive brain-computer interfaces for physiological text annotation to the real world.

AB - Automatic annotation of media content has become a critically important task for many digital services as the quantity of available online media content has grown exponentially. One approach is to annotate the content using the physiological responses of the media consumer. In the present paper, we reflect on three case studies that use brain signals for implicit text annotation to discuss the challenges faced when bringing passive brain-computer interfaces for physiological text annotation to the real world.

KW - Passive BCI

KW - Physiological computing

KW - Physiological text annotation

UR - http://www.scopus.com/inward/record.url?scp=85017099589&partnerID=8YFLogxK

U2 - 10.1145/3038439.3038445

DO - 10.1145/3038439.3038445

M3 - Conference article in proceedings

AN - SCOPUS:85017099589

SP - 9

EP - 13

BT - BCIforReal 2017 - Proceedings of the 2017 ACM Workshop on An Application-Oriented Approach to BCI Out of the Laboratory, co-located with IUI 2017

PB - ACM

T2 - International Conference on Intelligent User Interfaces

Y2 - 13 March 2017 through 16 March 2017

ER -

BCI for physiological text annotation

Abstract

Conference

Keywords

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Interactive machine learning from multiple biodata sources

Interactive machine learning from multiple biodata sources

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BCI for physiological text annotation

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Interactive machine learning from multiple biodata sources

Interactive machine learning from multiple biodata sources

Mediated haptic communication during social decision making: A neurophysiological perspective

Cite this