Thought2Text: Text Generation from EEG Brain Activities through Large Language Models
Authors: Shreya Shukla, Abhijit Mishra, Jose Torres, Jacek Gwizdka


Reviewer paper summaries: This paper introduces Thought-to-Text, that decodes brain activity (i.e., EEG) into text. The process involves three stages: 1) Training an EEG encoder to extract meaningful embeddings by minimizing cross-entropy loss and mean squared error; 2) fine-tuning LLMs with image embeddings; and 3) fine-tuning LLMs with EEG embeddings. In general, experimental results demonstrate the effectiveness of the proposed framework.
Summary of strengths: - This paper explores an interesting research topic.
- The writing is clear and easy to understand.

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After rebuttal:

I raised my score to 3.

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Summary of weaknesses: Please see the below questions.
Comments, suggestions and typos: - Are stage 1 and 2 (or stage 3) separate? In other words, is EEG ChannelNet trained or frozen during stages 2 or 3?
- The CVPR 2017 dataset contains image data and EEG signals, but no ground-truth text is available. Did you verify the correctness of the GPT-4 outputs?
- We can directly fine-tune LLMs with EEG embeddings without introducing stage 2. Have the authors tried this approach as an ablation study?
Overall assessment scores: 3