Improving BCI Performance after Classification

D. Plass - Oude Bos; Hayrettin Gürkök; B. Reuderink; Mannes Poel

doi:10.1145/2388676.2388799

Improving BCI Performance after Classification

D. Plass - Oude Bos
, Hayrettin Gürkök
, B. Reuderink
, Mannes Poel

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

3 Citations (Scopus)

158 Downloads (Pure)

Abstract

Brain-computer interfaces offer a valuable input modality, which unfortunately comes also with a high degree of uncertainty. There are simple methods to improve detection accuracy after the incoming brain activity has already been classified, which can be divided into (1) gathering additional evidence from other sources of information, and (2) transforming the unstable classification results to be more easy to control. The methods described are easy to implement, but it is essential to apply them in the right way. This paper provides an overview of the different techniques, showing where to apply them and comparing the effects. Detection accuracy is important, but there are trade-offs to consider. Future research should investigate the effectiveness of these methods in their context of use, as well as the optimal settings to obtain the right balance between functionality and meeting the user's expectations for maximum acceptance.

Original language	Undefined
Title of host publication	Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	587-594
Number of pages	8
ISBN (Print)	978-1-4503-1467-1
DOIs	https://doi.org/10.1145/2388676.2388799
Publication status	Published - Oct 2012
Event	14th International Conference on Multimodal Interaction, ICMI 2012 - Santa Monica, United States Duration: 22 Oct 2012 → 26 Oct 2012 Conference number: 14

Publication series

Name
Publisher	ACM

Conference

Conference	14th International Conference on Multimodal Interaction, ICMI 2012
Abbreviated title	ICMI
Country/Territory	United States
City	Santa Monica
Period	22/10/12 → 26/10/12

Keywords

HMI-MI: MULTIMODAL INTERACTIONS
IR-83667
debouncing
macro
predictive model
smoothing
EWI-22918
Brain-Computer Interfaces
Context
Hysteresis
Multimodal
dwell time
Post processing
METIS-296231
HMI-HF: Human Factors

Access to Document

10.1145/2388676.2388799

gcb132-plassPS1

Cite this

@inproceedings{91332bcb7f4e4ef3b9a9106b6af57af6,

title = "Improving BCI Performance after Classification",

abstract = "Brain-computer interfaces offer a valuable input modality, which unfortunately comes also with a high degree of uncertainty. There are simple methods to improve detection accuracy after the incoming brain activity has already been classified, which can be divided into (1) gathering additional evidence from other sources of information, and (2) transforming the unstable classification results to be more easy to control. The methods described are easy to implement, but it is essential to apply them in the right way. This paper provides an overview of the different techniques, showing where to apply them and comparing the effects. Detection accuracy is important, but there are trade-offs to consider. Future research should investigate the effectiveness of these methods in their context of use, as well as the optimal settings to obtain the right balance between functionality and meeting the user's expectations for maximum acceptance.",

keywords = "HMI-MI: MULTIMODAL INTERACTIONS, IR-83667, debouncing, macro, predictive model, smoothing, EWI-22918, Brain-Computer Interfaces, Context, Hysteresis, Multimodal, dwell time, Post processing, METIS-296231, HMI-HF: Human Factors",

author = "\{Plass - Oude Bos\}, D. and Hayrettin G{\"u}rk{\"o}k and B. Reuderink and Mannes Poel",

note = "10.1145/2388676.2388799 ; 14th International Conference on Multimodal Interaction, ICMI 2012 ; Conference date: 22-10-2012 Through 26-10-2012",

year = "2012",

month = oct,

doi = "10.1145/2388676.2388799",

language = "Undefined",

isbn = "978-1-4503-1467-1",

publisher = "Association for Computing Machinery",

pages = "587--594",

booktitle = "Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012",

address = "United States",

}

Plass - Oude Bos, D, Gürkök, H, Reuderink, B & Poel, M 2012, Improving BCI Performance after Classification. in Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012. Association for Computing Machinery, New York, pp. 587-594, 14th International Conference on Multimodal Interaction, ICMI 2012, Santa Monica, California, United States, 22/10/12. https://doi.org/10.1145/2388676.2388799

Improving BCI Performance after Classification. / Plass - Oude Bos, D.; Gürkök, Hayrettin; Reuderink, B. et al.
Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012. New York: Association for Computing Machinery, 2012. p. 587-594.

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

TY - GEN

T1 - Improving BCI Performance after Classification

AU - Plass - Oude Bos, D.

AU - Gürkök, Hayrettin

AU - Reuderink, B.

AU - Poel, Mannes

N1 - Conference code: 14

PY - 2012/10

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N2 - Brain-computer interfaces offer a valuable input modality, which unfortunately comes also with a high degree of uncertainty. There are simple methods to improve detection accuracy after the incoming brain activity has already been classified, which can be divided into (1) gathering additional evidence from other sources of information, and (2) transforming the unstable classification results to be more easy to control. The methods described are easy to implement, but it is essential to apply them in the right way. This paper provides an overview of the different techniques, showing where to apply them and comparing the effects. Detection accuracy is important, but there are trade-offs to consider. Future research should investigate the effectiveness of these methods in their context of use, as well as the optimal settings to obtain the right balance between functionality and meeting the user's expectations for maximum acceptance.

AB - Brain-computer interfaces offer a valuable input modality, which unfortunately comes also with a high degree of uncertainty. There are simple methods to improve detection accuracy after the incoming brain activity has already been classified, which can be divided into (1) gathering additional evidence from other sources of information, and (2) transforming the unstable classification results to be more easy to control. The methods described are easy to implement, but it is essential to apply them in the right way. This paper provides an overview of the different techniques, showing where to apply them and comparing the effects. Detection accuracy is important, but there are trade-offs to consider. Future research should investigate the effectiveness of these methods in their context of use, as well as the optimal settings to obtain the right balance between functionality and meeting the user's expectations for maximum acceptance.

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KW - macro

KW - predictive model

KW - smoothing

KW - EWI-22918

KW - Brain-Computer Interfaces

KW - Context

KW - Hysteresis

KW - Multimodal

KW - dwell time

KW - Post processing

KW - METIS-296231

KW - HMI-HF: Human Factors

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BT - Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012

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