Explainable Machine Learning Predictions for the Long-term Performance of Brain-Computer Interfaces

Morgan E Urdaneta, Nicole C Veit, Renae G Burke, Ian G Malone, Kaleb E Smith, Kevin J Otto

Keywords: SHAP, Explainability, feature importance, BCI, Neural interfaces, longitudinal intracortical recordings, neural engineering

TL;DR: Development of an explainable AI pipeline that can predict with high accuracy and elucidate the most important factors involved in the long-term stability of intracortical brain computer interfaces

Abstract: Brain computer interfaces (BCIs) can decode neural signals to control assistive technologies such as robotic limbs for people with paralysis. Neural recordings from intracortical microelectrodes offer the spatiotemporal resolution (e.g., sortable units) necessary for complex tasks, such as controlling a robotic arm with multiple degrees of freedom. However, the quality of these signals decays over time despite many attempts to prolong their longevity. This decrease in long-term performance limits the implementation of this potentially beneficial technology. Predicting whether a channel will have sortable units across time would mitigate this issue and increase the utility of these devices by reducing uncertainty, yet to-date, no such methods exist. Similarly, it would be useful to understand how variables like time post-implantation, electrochemical characteristics, and electrode design impact the long-term quality of these signals. Here, we obtained longitudinal neural recordings and electrochemical data from freely behaving rats implanted with a custom designed microelectrode array with varying site areas, shank positions, and site depths. This dataset was used to develop an explainable artificial intelligence pipeline that predicts with high accuracy the presence of sortable units on a given channel and elucidates the most important factors leading to these predictions. Our pipeline was able to predict whether a channel will be active with an AUC of 0.79 (95% C.I. 0.73–0.86) on unseen data. The most important features of the model were experimental subject, time post-implantation, and a channel’s previous spike metrics. Electrode site depth was the most important electrode design variable. Our results demonstrate the feasibility of implementing explainable artificial intelligence pipelines for longitudinal BCI studies and support previous reports on how factors like time, inter-animal variability, and cortical depth impact long-term performance of BCIs. These results are an important step forward in improving efficient decoding performance and guiding device development, which stand to advance the field and benefit the lives of human BCI patients.

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