Machine Learning-Based Early Prediction of Neural Microelectrode Lifetime Under Accelerated Aging Conditions

A 10-feature Elastic Net model predicts impedance-defined neural microelectrode lifetime from early-life measurements through Day 5 with 3.71 accelerated-aging days RMSE and 13.90% MAPE.

• Why it matters: Long-lived chronically implantable microelectrodes are hard to validate experimentally within practical preclinical timelines, even with accelerated life testing.
• What they did: The study built an Elastic Net regression model using early-life feature engineering and evaluated device-level prediction using Day 5 electrochemical measurements, plus independent-batch covariance alignment and calibration with five labeled channels.
• The result: In device-level validation, errors were 3.71 days RMSE and 13.90% MAPE; in an independent fabrication batch, calibration reduced MAPE from 38.1% to 22.5% for Small electrodes and from 56.6% to 18.2% for Medium electrodes.

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