SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval
Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction. Most existing Brain-Computer Interface (BCI) approaches rely on memory-intensive fine-tuning of Large Language Models (LLMs) or encoder-decoder models on raw EEG signals, resulting in expensive training pipelines, limited accessibility, and potential exposure of sensitive neural data. We introduce SENSE (SEmantic Neural Sparse Extraction), a lightweight and privacy-preserving framework that translates non-invasive electroencephalography (EEG) into text without LLM fine-tuning. SENSE decouples decoding into two sta...
| Authors | Akshaj Murhekar, Christina Liu, Abhijit Mishra et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 29 July 2026 |
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