@inproceedings{xi-etal-2023-unicorn,
title = "{U}ni{C}o{RN}: Unified Cognitive Signal {R}econstructio{N} bridging cognitive signals and human language",
author = "Xi, Nuwa and
Zhao, Sendong and
Wang, Haochun and
Liu, Chi and
Qin, Bing and
Liu, Ting",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.741",
doi = "10.18653/v1/2023.acl-long.741",
pages = "13277--13291",
abstract = "Decoding text stimuli from cognitive signals (e.g. fMRI) enhances our understanding of the human language system, paving the way for building versatile Brain-Computer Interface. However, existing studies largely focus on decoding individual word-level fMRI volumes from a restricted vocabulary, which is far too idealized for real-world application. In this paper, we propose fMRI2text, the first open-vocabulary task aiming to bridge fMRI time series and human language. Furthermore, to explore the potential of this new task, we present a baseline solution, UniCoRN: the Unified Cognitive Signal ReconstructioN for Brain Decoding. By reconstructing both individual time points and time series, UniCoRN establishes a robust encoder for cognitive signals (fMRI {\&} EEG). Leveraging a pre-trained language model as decoder, UniCoRN proves its efficacy in decoding coherent text from fMRI series across various split settings. Our model achieves a 34.77{\%} BLEU score on fMRI2text, and a 37.04{\%} BLEU when generalized to EEG-to-text decoding, thereby surpassing the former baseline. Experimental results indicate the feasibility of decoding consecutive fMRI volumes, and the effectiveness of decoding different cognitive signals using a unified structure.",
}
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%0 Conference Proceedings
%T UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language
%A Xi, Nuwa
%A Zhao, Sendong
%A Wang, Haochun
%A Liu, Chi
%A Qin, Bing
%A Liu, Ting
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xi-etal-2023-unicorn
%X Decoding text stimuli from cognitive signals (e.g. fMRI) enhances our understanding of the human language system, paving the way for building versatile Brain-Computer Interface. However, existing studies largely focus on decoding individual word-level fMRI volumes from a restricted vocabulary, which is far too idealized for real-world application. In this paper, we propose fMRI2text, the first open-vocabulary task aiming to bridge fMRI time series and human language. Furthermore, to explore the potential of this new task, we present a baseline solution, UniCoRN: the Unified Cognitive Signal ReconstructioN for Brain Decoding. By reconstructing both individual time points and time series, UniCoRN establishes a robust encoder for cognitive signals (fMRI & EEG). Leveraging a pre-trained language model as decoder, UniCoRN proves its efficacy in decoding coherent text from fMRI series across various split settings. Our model achieves a 34.77% BLEU score on fMRI2text, and a 37.04% BLEU when generalized to EEG-to-text decoding, thereby surpassing the former baseline. Experimental results indicate the feasibility of decoding consecutive fMRI volumes, and the effectiveness of decoding different cognitive signals using a unified structure.
%R 10.18653/v1/2023.acl-long.741
%U https://aclanthology.org/2023.acl-long.741
%U https://doi.org/10.18653/v1/2023.acl-long.741
%P 13277-13291
Markdown (Informal)
[UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language](https://aclanthology.org/2023.acl-long.741) (Xi et al., ACL 2023)
ACL