Hierarchical EEG-to-text alignment for sentence-level decoding
Original title: Thought-deciphering via hierarchical EEG-to-text alignment
A novel hierarchical alignment framework retrieves read sentences from EEG signals and context for brain-computer interfaces.
Communication disabilities impair well-being and require non-invasive brain-to-text interfaces. Most breakthroughs use costly fMRI or MEG; EEG offers a cheaper alternative yet prior work stayed below sentence level. This paper introduces a hierarchical framework to classify sentences at the retrieval level using EEG and context.
- Sentence classification via EEG is under-investigated compared to generation
- Sentences carry high-level information even with missing words
- Framework aligns EEG-to-text hierarchically for retrieval
From the original article
1 min read at arxiv.orgCommunication is important for our well-being, but it can be impaired by disabilities, such as locked-in syndrome (LIS), cerebral palsy (CP), and dysarthria. Non-invasive brain-to-text brain-computer interfaces (BCIs) offer a promising solution by providing an alternative communication pathway for people with communication disabilities.
By Xiaopeng Mao · arxiv.org
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