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Leveraging repeated EEG responses across sessions, this method achieves remarkable session-invariance and reduces character error rates in EEG-to-speech decoding.
Explicitly modeling the dependency between amplitude and phase allows PolarBM to significantly outperform traditional models in audio signal accuracy.
