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  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
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  "notes": "Includes specific cost estimates per hour for different neuroimaging modalities.",
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      "text": "The most precise devices yield the best absolute decoding (7T > 3T > MEG > EEG), but deep nets deliver the largest gains on noisy modalities, narrowing the gap.\nScaling laws: performance rises log-linearly with more recording time. The returns come chiefly from recording more per subject, not recruiting more participants.\nCost model (rough estimates): ~$263/hr EEG, $550/hr MEG, $935/hr 3T, $1,093/hr 7T. A $131k budget buys markedly different accuracy across modalities, so optimal scaling depends on budget and target fidelity.",
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      "text": "Pearson R: $1,093/hr",
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      "text": "Meta AI benchmarked brain-to-image decoding across EEG, MEG, 3T fMRI and 7T fMRI using 8 public datasets, 84 volunteers, 498 hours of recordings, and 2.3M image-evoked responses, evaluated in single-trial settings. They find no performance plateau: decoding improves roughly log-linearly with more recording, and gains depend mostly on data per subject rather than adding subjects. Deep learning helps most on the noisiest sensors (EEG/MEG). Estimated dollar-per-hour costs show 7T isn't always the most cost-effective path.",
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