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Reading Task Classification Using EEG and Eye-Tracking Data
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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.
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Cited by 1 Pith paper
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ETS: Open Vocabulary Electroencephalography-To-Text Decoding and Sentiment Classification
ETS pairs EEG and eye-tracking with a CNN-transformer encoder and BART/T5 decoder to decode read sentences from brain signals and classify their sentiment in a zero-shot pipeline.
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