UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language
Year: 2023
Type: conference-paper
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 openvocabulary 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 EEGto-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.
Institution: Harbin Institute of Technology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Award: G6081050233
OpenAlex ID: W4385570408