Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)

TL;DR

Thought2Text introduces a novel method that leverages fine-tuned large language models and EEG data to decode brain activity into coherent text, advancing brain-computer interface technology.

Contribution

The paper presents a new multimodal approach combining EEG encoding and LLM fine-tuning to generate text directly from brain signals, a significant step forward in neural decoding.

Findings

Effective text generation from EEG signals demonstrated

Multimodal LLMs outperform traditional methods

Potential for portable brain-computer interfaces

Abstract

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 and text captions demonstrate the efficacy of multimodal LLMs (LLaMA-v3, Mistral-v0.3, Qwen2.5), validated using traditional language generation evaluation metrics, as well as fluency and adequacy measures. This approach marks a significant advancement towards portable, low-cost "thoughts-to-text" technology with potential applications in both neuroscience and natural language processing.