The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data.

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.

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing.

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet.

Neural dynamics of sentiment processing during naturalistic sentence reading.

When we read, our eyes move through the text in a series of fixations and high-velocity saccades to extract visual information. This process allows the brain to obtain meaning, e.g.

ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading.

We present the Zurich Cognitive Language Processing Corpus (ZuCo), a dataset combining electroencephalography (EEG) and eye-tracking recordings from subjects reading natural sentences. ZuCo includes high-density EEG and eye-tracking data of 12 healthy adult native English speakers, each reading natural English text for 4-6 hours. The recordings span two normal reading tasks and one task-specific reading task, resulting in a dataset that encompasses EEG and eye-tracking data of 21,629 words in 1107 sentences and 154,173 fixations.