Dunlap Collin F, Colachis Samuel C, Meyers Eric C, Bockbrader Marcia A, Friedenberg David A
Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States.
Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States.
Front Neurorobot. 2020 Oct 9;14:558987. doi: 10.3389/fnbot.2020.558987. eCollection 2020.
Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.
脑机接口（BMI）记录神经活动并将其转化为控制信号，用于辅助设备或其他设备。皮层内微电极阵列（MEA）通过提供高分辨率神经记录，实现对复杂任务的高自由度BMI控制。然而，长期植入的MEA会处于动态环境中，短暂或系统性干扰可能会干扰神经记录并降低BMI性能。通常，神经植入失败模式可分为生物、材料或机械类。虽然这种分类有助于深入了解干扰的因果病因，但对于理解对BMI功能的影响程度或可能的补偿策略帮助较小。因此，我们提出了一种针对皮层内记录干扰的补充分类框架，该框架基于对BMI性能的影响持续时间以及干预的需求和响应性：（1）在数分钟到数小时的时间尺度上干扰记录，且可自发解决；（2）对记录造成持续干扰，但根本原因可通过适当干预得以纠正；（3）导致信号质量持续或逐渐下降，但其对BMI性能的影响可通过算法减轻；（4）导致永久性信号丢失，无法补救或补偿。这种皮层内BMI干扰类型的概念化有助于突出潜在硬件改进的特定领域，并识别算法干预的机会。我们回顾了已报道的MEA记录干扰情况，并展示了干扰生物、材料和机械机制如何根据其对信号特征的影响进一步分类。然后我们讨论了针对每种提出的干扰类别的潜在补偿协议。具体而言，短暂干扰可通过使用强大的神经解码器特征、数据增强方法、自适应机器学习模型和专门的信号参考技术来最小化。统计过程控制方法可识别可修复的干扰以便快速干预。诸如阻抗谱等诊断方法可为神经特征选择和解码模型提供信息，以补偿不可逆干扰。针对不可逆干扰的其他补偿策略包括信息挽救技术、解码器训练期间的数据增强以及降低受损通道权重的自适应解码方法。
