PNA decomposes MEG recordings via ICA, isolates artifact components using EOG/ECG references, and re-injects scaled artifacts into clean data to train decoders that are invariant to physiological noise, improving imagined-digit classification by 4.7 percentage points with EEGNet.
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2 Pith papers cite this work, alongside 192 external citations. Polarity classification is still indexing.
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Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech
PNA decomposes MEG recordings via ICA, isolates artifact components using EOG/ECG references, and re-injects scaled artifacts into clean data to train decoders that are invariant to physiological noise, improving imagined-digit classification by 4.7 percentage points with EEGNet.
- MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis