A rigorous leave-one-subject-out benchmark on public auditory EEG data shows five-vowel decoding accuracy of 25.5 percent (chance 20 percent) using differential entropy features and LightGBM, with vowel information present but weak and localized to early auditory transients.
Decoding speech perception from non-invasive brain recordings.Nature Machine Intelligence, 5(10):1097–1107
2 Pith papers cite this work. Polarity classification is still indexing.
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Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
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How Well Can We Decode Vowels from Auditory EEG -- A Rigorous Cross-Subject Benchmark with Honest Assessment
A rigorous leave-one-subject-out benchmark on public auditory EEG data shows five-vowel decoding accuracy of 25.5 percent (chance 20 percent) using differential entropy features and LightGBM, with vowel information present but weak and localized to early auditory transients.
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Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity
Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.