BrainCoDe

Electroencephalography-based Comprehension Detection during Reading and Listening

Conference paper (2020)

Authors

C. Schneegass Ludwig Maximilians University
Thomas Kosch Ludwig Maximilians University
Andrea Baumann Ludwig Maximilians University
Marius Rusu Ludwig Maximilians University
Mariam Hassib Ludwig Maximilians University
Heinrich Hussmann Ludwig Maximilians University
Affiliation
External organisation
EEG Implicit comprehension detection Language learning
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Published Date

2020

Language

English

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Affiliation

External organisation

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.