EEG-based neural decoding of Intelligence Questions

Isabella Schlattner; Achim Leydecker; Felix Biesmann; Yiyu Chen; Siamac Fazli

doi:10.1109/IWW-BCI.2015.7073025

EEG-based neural decoding of Intelligence Questions

Isabella Schlattner, Achim Leydecker, Felix Biesmann, Yiyu Chen, Siamac Fazli

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Decoding mental processes in single trials is one of the prerequisites for tailoring learning paradigms, which aim at improving performance in cognitive tasks. In this study user choices are predicted in a matrix reasoning task. By employing multivariate analysis techniques we are able to show that it is possible to decode the subjects' answers prior to their response by means of ERP-based EEG data. Our results indicate that parieto-occipital ERP components are most discriminative and lead to an average single-trial accuracy of 67.7 ± 6.3% across 5 subjects.

Original language	English
Title of host publication	3rd International Winter Conference on Brain-Computer Interface, BCI 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781479974948
DOIs	https://doi.org/10.1109/IWW-BCI.2015.7073025
Publication status	Published - 2015 Mar 30
Event	2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of Duration: 2015 Jan 12 → 2015 Jan 14

Other

Other	2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
Country/Territory	Korea, Republic of
City	Gangwon-Do
Period	15/1/12 → 15/1/14

ASJC Scopus subject areas

Human-Computer Interaction
Cognitive Neuroscience
Sensory Systems

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10.1109/IWW-BCI.2015.7073025

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EEG-based neural decoding of Intelligence Questions. / Schlattner, Isabella; Leydecker, Achim; Biesmann, Felix; Chen, Yiyu; Fazli, Siamac.

3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7073025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Schlattner, I, Leydecker, A, Biesmann, F, Chen, Y & Fazli, S 2015, EEG-based neural decoding of Intelligence Questions. in 3rd International Winter Conference on Brain-Computer Interface, BCI 2015., 7073025, Institute of Electrical and Electronics Engineers Inc., 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015, Gangwon-Do, Korea, Republic of, 15/1/12. https://doi.org/10.1109/IWW-BCI.2015.7073025

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abstract = "Decoding mental processes in single trials is one of the prerequisites for tailoring learning paradigms, which aim at improving performance in cognitive tasks. In this study user choices are predicted in a matrix reasoning task. By employing multivariate analysis techniques we are able to show that it is possible to decode the subjects' answers prior to their response by means of ERP-based EEG data. Our results indicate that parieto-occipital ERP components are most discriminative and lead to an average single-trial accuracy of 67.7 ± 6.3% across 5 subjects.",

author = "Isabella Schlattner and Achim Leydecker and Felix Biesmann and Yiyu Chen and Siamac Fazli",

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N2 - Decoding mental processes in single trials is one of the prerequisites for tailoring learning paradigms, which aim at improving performance in cognitive tasks. In this study user choices are predicted in a matrix reasoning task. By employing multivariate analysis techniques we are able to show that it is possible to decode the subjects' answers prior to their response by means of ERP-based EEG data. Our results indicate that parieto-occipital ERP components are most discriminative and lead to an average single-trial accuracy of 67.7 ± 6.3% across 5 subjects.

AB - Decoding mental processes in single trials is one of the prerequisites for tailoring learning paradigms, which aim at improving performance in cognitive tasks. In this study user choices are predicted in a matrix reasoning task. By employing multivariate analysis techniques we are able to show that it is possible to decode the subjects' answers prior to their response by means of ERP-based EEG data. Our results indicate that parieto-occipital ERP components are most discriminative and lead to an average single-trial accuracy of 67.7 ± 6.3% across 5 subjects.

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EEG-based neural decoding of Intelligence Questions

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