While deep learning has enabled language decoding from intracranial brain recordings, extending this capability to non-invasive recordings remains an unresolved challenge. Decoding individual words from non-invasive brain recordings is particularly difficult, as word-level neural evidence is weak, temporally distribute...
Yue-Yang Li, Shu-Ran Chen, W. Siok et al.· 1 citation
In most individuals, language is predominantly left-lateralized, a prominent feature of human brain organization. Recent research increasingly views language lateralization as a property of distributed and interacting functional networks. In this article, we review evidence that the lateralization of functional connect...
Wen-Fei Cao, Ni-Zhuan Wang, Ke Zhou et al.· Trends in Neurosciences· 0 citations
Intracranial electroencephalography (iEEG) provides temporally precise and spatially specific access to neural activity from focal and deep brain regions, but its invasiveness and restricted anatomical coverage limit routine use. These constraints have motivated scalp-to-intracranial inference, termed virtual iEEG when...
Dong-Yi He, Xiang-Kai Wang, Hong-Jie Yan et al.· 0 citations
Electroencephalography (EEG) provides a noninvasive means of capturing emotion-related neural dynamics, yet reliable EEG emotion decoding lacks models that can both generalize to unseen individuals and populations while preserving neural interpretability. To address these challenges, EmoDiPyraTrans is proposed as a dev...
Dong-Yi He, Bin Jiang, Xiang-Kai Wang et al.· 0 citations
A controllable dysarthric speech synthesis framework for ASR augmentation with separate prompt-derived timbre prefixes and learnable patient-specific pathology prefixes that provides effective speaker-diverse augmentation when combined with real data.
Hao-Shen Wang, Xuelin Zhong, Bing-Bing Lin et al.· 0 citations
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