The Signal Drifts, the Representation Holds
TL;DR for operators A neural interface that works during calibration can become less reliable when brain recordings change across sessions. Guo and colleagues’ Neural Decoding as Cognitive Inference1 investigates an alternative to repeatedly correcting those recordings: infer a higher-level cognitive representation constrained by the brain’s cortical geometry. Across three longitudinal datasets, the inferred representations exhibited substantially higher cross-session similarity and lower displacement than the original observation-level representations. Cross-session decoding also outperformed selected baselines. ...