Decoding Part-of-Speech from Human EEG Signals

Alex Murphy; Bernd Bohnet; Ryan McDonald; Uta Noppeney

ACL 2022long24 citations

Decoding Part-of-Speech from Human EEG Signals

Alex Murphy, Bernd Bohnet, Ryan McDonald, Uta Noppeney

Abstract

This work explores techniques to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution with electroencephalography (EEG) during text reading. We first show that information about word length, frequency and word class is encoded by the brain at different post-stimulus latencies. We then demonstrate that pre-training on averaged EEG data and data augmentation techniques boost PoS decoding accuracy for single EEG trials. Finally, applying optimised temporally-resolved decoding techniques we show that Transformers substantially outperform linear-SVMs on PoS tagging of unigram and bigram data.

BibTeX

@inproceedings{murphy-etal-2022-decoding,
    title = "Decoding Part-of-Speech from Human {EEG} Signals",
    author = "Murphy, Alex  and
      Bohnet, Bernd  and
      McDonald, Ryan  and
      Noppeney, Uta",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.156/",
    doi = "10.18653/v1/2022.acl-long.156",
    pages = "2201--2210"
}