@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Decoding Part-of-Speech from Human EEG Signals
%A Murphy, Alex
%A Bohnet, Bernd
%A McDonald, Ryan
%A Noppeney, Uta
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F murphy-etal-2022-decoding
%X 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.
%R 10.18653/v1/2022.acl-long.156
%U https://aclanthology.org/2022.acl-long.156
%U https://doi.org/10.18653/v1/2022.acl-long.156
%P 2201-2210
Markdown (Informal)
[Decoding Part-of-Speech from Human EEG Signals](https://aclanthology.org/2022.acl-long.156) (Murphy et al., ACL 2022)
ACL
- Alex Murphy, Bernd Bohnet, Ryan McDonald, and Uta Noppeney. 2022. Decoding Part-of-Speech from Human EEG Signals. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2201–2210, Dublin, Ireland. Association for Computational Linguistics.