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arXiv Machine Learning By Blessing Isoyiza Adeika, Onyema Osuagwu

Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora

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The study evaluated whether imagined speech decoded from scalp EEG can transfer across individuals using three public datasets. Across 482 participant pairs, cross‑subject decoding accuracy was essentially at chance, while within‑subject decoding only surpassed chance in one dataset. The results suggest that imagined‑speech content does not generalize between people and that larger single‑condition datasets are more beneficial than further model development.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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