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NeuroSonic introduces a conditional flow-matching framework for reconstructing continuous speech from EEG signals, addressing the structural mismatch between neural and acoustic data by learning a deterministic probability-flow velocity field. It achieves up to 26.3% improvement in perceptual quality over existing GAN, diffusion, and mean-flow baselines on cross-subject benchmarks.