@article{HollensteinTrondlePlomeckaetal.2023,
  author    = {Hollenstein, Nora and Trondle, Marius and Plomecka, Martyna and Kiegeland, Samuel and Ozyurt, Yilmazcan and J{\"a}ger, Lena Ann and Langer, Nicolas},
  title     = {The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data},
  series = {Frontiers in psychology},
  volume    = {13},
  journal   = {Frontiers in psychology},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2022.1028824},
  pages     = {20},
  year      = {2023},
  abstract  = {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: .},
  language  = {en}
}