Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding
arXiv:2603.03312v3 Announce Type: replace Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain constrained by three fundamental issues: Semantic Bias, where outputs collapse into generic linguistic templates; Signal Neglect, where models rely heavily on LLM priors to hallucinate fluent text even in the absence of meaningful signals; and the "BLEU Trap", where high-frequency stopwords inflate n-gram metrics, masking a lack of true semantic fidelity. To resolve these challenges, we move beyond conve
The increasing sophistication of EEG signal processing combined with advanced AI models makes direct brain-to-text decoding a current research focus.
Improving EEG-to-text decoding beyond current limitations could revolutionize human-computer interaction, communication for impaired individuals, and potentially AI control methods.
This research suggests a move towards more semantically accurate and signal-grounded brain-to-text interfaces, overcoming prior issues of generic outputs and over-reliance on language model priors.
- · Brain-computer interface developers
- · Patients with communication disorders
- · AI-powered assistive technology providers
- · Neuroscience research institutions
- · Companies relying on less accurate neural decoding methods
- · Developers of generic AI text generation from limited neural input
More reliable and less 'hallucinatory' brain-to-text communication systems could emerge from this framework.
This could lead to new forms of human-AI collaboration where user intent is directly and accurately inferred from neural signals.
The ability to decode complex thoughts more accurately might raise ethical questions about mental privacy and the nature of consciousness itself.
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Read at arXiv cs.CL