INTELLIGENT QUALITY-AWARE MULTI-ELECTRODE POSITIONING WITH NEURAL SIGNAL PREDICTION AND DRIFT-AWARE ADAPTIVE OPTIMIZATION

Abstract

In addition to signal noise and variability in their temporal characteristics, long-term extracellular recordings of neural activity are also influenced by the relative position of recording electrodes to the source of the neurons. Historically, quality-aware repositioning of electrodes has been accomplished after the fact (post-hoc) when the quality of the recorded signal fell below a predetermined threshold. This leads to periods of non-optimal recording quality and delays in accurately repositioning the electrodes. To address these issues, we propose an Intelligent Quality-Aware Multi-Electrode Positioning (IQ-MEP) predictive methodology that integrates techniques for predicting the quality of a neural signal, estimating drift of electrodes, and adaptively optimizing electrode position. IQ-MEP employs historical features of the neural recordings (i. e., quality, signal-to-noise ratio, spike-related features. And electrode position) to predict future recording quality as well as identify potential quality degradations before quality reaches a critical threshold. Using both the predicted quality and the estimated drift direction and magnitude, an adaptive optimization module determines the future optimal position of the electrode(s) and proactively repositions them. A browser-based simulation environment has been developed to establish a reproducible experimental platform for simulating virtual neurons, electrode/tetrode configurations, controllable noise, spatial drift, variability in recording quality, and multi-electrode positioning. The framework supports a comparison between static, reactive threshold-based, and predictive electrode repositioning strategies under controlled drift and noise conditions. Performance metrics employed to evaluate the effectiveness of the proposed predictive methodology include prediction error of future quality prediction, accuracy of estimating drift direction, positional error of electrodes, early warning time, quality retention, recovery time, and movement efficiency of electrodes. This proposed predictive methodology provides a software-based approach to assess whether predictive analysis of neural signals may be employed to support proactive repositioning of electrodes to improve stability of long term recordings of extracellular neural activity under simulated drift conditions.