TY  - JOUR
A1  - Hollenstein, Nora
A1  - Trondle, Marius
A1  - Plomecka, Martyna
A1  - Kiegeland, Samuel
A1  - Ozyurt, Yilmazcan
A1  - Jäger, Lena Ann
A1  - Langer, Nicolas
T1  - The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data
T2  - Frontiers in psychology
N2  - We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: .
KW  - reading task classification
KW  - eye-tracking
KW  - EEG
KW  - machine learning
KW  - reading research
KW  - cross-subject evaluation
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64219
SN  - 1664-1078
VL  - 13
PB  - Frontiers Media
CY  - Lausanne
ER  -