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BrainCoDe: Electroencephalography-based Comprehension Detection during Reading and Listening

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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BrainCoDe: Electroencephalography-based Comprehension Detection during Reading and Listening. / Schneegass, Christina; Kosch, Thomas; Baumann, Andrea et al.
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York: ACM, 2020. p. 1-13.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Schneegass, C, Kosch, T, Baumann, A, Rusu, M, Hassib, M & Hussmann, H 2020, BrainCoDe: Electroencephalography-based Comprehension Detection during Reading and Listening. in CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, New York, pp. 1-13. https://doi.org/10.1145/3313831.3376707

APA

Schneegass, C., Kosch, T., Baumann, A., Rusu, M., Hassib, M., & Hussmann, H. (2020). BrainCoDe: Electroencephalography-based Comprehension Detection during Reading and Listening. In CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13). ACM. https://doi.org/10.1145/3313831.3376707

Vancouver

Schneegass C, Kosch T, Baumann A, Rusu M, Hassib M, Hussmann H. BrainCoDe: Electroencephalography-based Comprehension Detection during Reading and Listening. In CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York: ACM. 2020. p. 1-13 doi: 10.1145/3313831.3376707

Author

Schneegass, Christina ; Kosch, Thomas ; Baumann, Andrea et al. / BrainCoDe : Electroencephalography-based Comprehension Detection during Reading and Listening. CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York : ACM, 2020. pp. 1-13

Bibtex

@inproceedings{e18ce661e6e14e91a8164e586260aaae,
title = "BrainCoDe: Electroencephalography-based Comprehension Detection during Reading and Listening",
abstract = "The pervasive availability of media in foreign languages is a rich resource for language learning. However, learners are forced to interrupt media consumption whenever comprehension problems occur. We present BrainCoDe, a method to implicitly detect vocabulary gaps through the evaluation of event-related potentials (ERPs). In a user study (N=16), we evaluate BrainCoDe by investigating differences in ERP amplitudes during listening and reading of known words compared to unknown words. We found significant deviations in N400 amplitudes during reading and in N100 amplitudes during listening when encountering unknown words. To evaluate the feasibility of ERPs for real-time applications, we trained a classifier that detects vocabulary gaps with an accuracy of 87.13% for reading and 82.64% for listening, identifying eight out of ten words correctly as known or unknown. We show the potential of BrainCoDe to support media learning through instant translations or by generating personalized learning content.",
author = "Christina Schneegass and Thomas Kosch and Andrea Baumann and Marius Rusu and Mariam Hassib and Heinrich Hussmann",
year = "2020",
month = apr,
day = "23",
doi = "10.1145/3313831.3376707",
language = "English",
pages = "1--13",
booktitle = "CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems",
publisher = "ACM",

}

RIS

TY - GEN

T1 - BrainCoDe

T2 - Electroencephalography-based Comprehension Detection during Reading and Listening

AU - Schneegass, Christina

AU - Kosch, Thomas

AU - Baumann, Andrea

AU - Rusu, Marius

AU - Hassib, Mariam

AU - Hussmann, Heinrich

PY - 2020/4/23

Y1 - 2020/4/23

N2 - The pervasive availability of media in foreign languages is a rich resource for language learning. However, learners are forced to interrupt media consumption whenever comprehension problems occur. We present BrainCoDe, a method to implicitly detect vocabulary gaps through the evaluation of event-related potentials (ERPs). In a user study (N=16), we evaluate BrainCoDe by investigating differences in ERP amplitudes during listening and reading of known words compared to unknown words. We found significant deviations in N400 amplitudes during reading and in N100 amplitudes during listening when encountering unknown words. To evaluate the feasibility of ERPs for real-time applications, we trained a classifier that detects vocabulary gaps with an accuracy of 87.13% for reading and 82.64% for listening, identifying eight out of ten words correctly as known or unknown. We show the potential of BrainCoDe to support media learning through instant translations or by generating personalized learning content.

AB - The pervasive availability of media in foreign languages is a rich resource for language learning. However, learners are forced to interrupt media consumption whenever comprehension problems occur. We present BrainCoDe, a method to implicitly detect vocabulary gaps through the evaluation of event-related potentials (ERPs). In a user study (N=16), we evaluate BrainCoDe by investigating differences in ERP amplitudes during listening and reading of known words compared to unknown words. We found significant deviations in N400 amplitudes during reading and in N100 amplitudes during listening when encountering unknown words. To evaluate the feasibility of ERPs for real-time applications, we trained a classifier that detects vocabulary gaps with an accuracy of 87.13% for reading and 82.64% for listening, identifying eight out of ten words correctly as known or unknown. We show the potential of BrainCoDe to support media learning through instant translations or by generating personalized learning content.

U2 - 10.1145/3313831.3376707

DO - 10.1145/3313831.3376707

M3 - Conference contribution/Paper

SP - 1

EP - 13

BT - CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

PB - ACM

CY - New York

ER -