Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)
<|reference_start|>Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs): Decoding and expressing brain activity in a comprehensible form is a challenging frontier in AI. This paper presents Thought2Text, which uses instruction-tuned Large Language Models (LLMs) fine-tuned with EEG data to achieve this goal. The approach involves three stages: (1) training an EEG encoder for visual feature extraction, (2) fine-tuning LLMs on image and text data, enabling multimodal description generation, and (3) further fine-tuning on EEG embeddings to generate text directly from EEG during inference. Experiments on a public EEG dataset collected for six subjects with image stimuli demonstrate the efficacy of multimodal LLMs (LLaMa-v3, Mistral-v0.3, Qwen2.5), validated using traditional language generation evaluation metrics, GPT-4 based assessments, and evaluations by human expert. This approach marks a significant advancement towards portable, low-cost "thoughts-to-text" technology with potential applications in both neuroscience and natural language processing (NLP).<|reference_end|>
@article{mishra2024thought2text:,
    title={Thought2Text: Text Generation from EEG Signal using Large Language
  Models (LLMs)},
    author={Abhijit Mishra, Shreya Shukla, Jose Torres, Jacek Gwizdka, Shounak
  Roychowdhury},
    journal={arXiv preprint arXiv:2410.07507},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07507},
    primaryClass={cs.CL}
}