# Reading Task Classification Using EEG and Eye-Tracking Data

arXiv: https://arxiv.org/abs/2112.06310 · HTML: https://mlsanity.com/paper/2112.06310/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 12 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [CogniVal: A Framework for Cognitive Word Embedding Evaluation](https://mlsanity.com/paper/1909.09001/) (2019)
- [Relative Importance in Sentence Processing](https://mlsanity.com/paper/2106.03471/) (2021)
- [LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach](https://mlsanity.com/paper/2104.13043/) (2021)
- [Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification](https://mlsanity.com/paper/2102.11922/) (2021)

## Built on by (most cited first, 5 of 5)

- [Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models](https://mlsanity.com/paper/2210.09819/) (2022)
- [On Creating A Brain-To-Text Decoder](https://mlsanity.com/paper/2501.06326/) (2025)
- [ETS: Open Vocabulary Electroencephalography-To-Text Decoding and Sentiment Classification](https://mlsanity.com/paper/2506.14783/) (2025)
- [On the Role of Activation Functions in EEG-To-Text Decoder](https://mlsanity.com/paper/2410.12572/) (2024)
- [Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading](https://mlsanity.com/paper/2505.02872/) (2025)

## Abstract (arXiv)

The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading. We analyze whether machine learning methods are able to classify these two tasks using eye-tracking and EEG features. We implement models with aggregated sentence-level features as well as fine-grained word-level features. We test the models in within-subject and cross-subject evaluation scenarios. All models are tested on the ZuCo 1.0 and ZuCo 2.0 data subsets, which are characterized by differing recording procedures and thus allow for different levels of generalizability. Finally, we provide a series of control experiments to analyze the results in more detail.