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

Key points

• A decoder of imagined speech that works for a new user without calibration would make a non-invasive speech interface practical.1
• We analyzed three public corpora, ds003626, KaraOne and Track 3 of the 2020 International Brain-Computer Interface Competition, under a protocol that enforces split integrity by assertion and reports exact binomial thresholds, group tests and bootstrap confidence intervals.1
• We trained decoders on each participant and tested them on every other, compared representational geometry across participants without a classifier, applied subject-level alignment, and tested five explanations for the within-subject ceiling.1
• Imagery against pre-stimulus rest generalized at about 0.61 after artifact and elapsed-time controls, but its decoder labelled the interval preceding imagery as imagery as often as imagery itself, so the contrast reflects a post-stimulus state, not imagery.1

Sources (1)

• [1]Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 09:25 PM

  We tested whether imagined-speech content recorded with scalp electroencephalography (EEG) transfers across people, and examined why accuracy within a single person remains low.

  A decoder of imagined speech that works for a new user without calibration would make a non-invasive speech interface practical.

Extractive summary: sentences quoted from the sources.