Figure 2.

Classification of common MEA signal disruptions and applicable compensatory strategies. (A) Signal disruptions are classified according to their underlying cause (Biological, Material, or Mechanical), and impact on signal quality and responsiveness to intervention (Transient, Reparable, Irreversible Compensable, and Irreversible Non-compensable). (B) Signal disruptions can be explicitly detected with statistical monitoring of neural features and recording metrics. Following the detection of a disruption, BMIs can initiate tailored algorithmic countermeasures to adapt to changes in signal characteristics. In parallel, advanced machine learning algorithms and decoder training strategies mitigate the effect of disruptions without requiring explicit detection. (C) The newly proposed disruption classes have characteristic interventions that help maintain BMI performance. Signal preprocessing, data augmentation, neural feature selection, neural manifolds, and adaptive neural decoders are among the most useful techniques for mitigating the effects of recording disruptions.