Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review

Abstract

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 in vivo 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) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions 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. In-vivo 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.

Explant Analysis of Utah Electrode Arrays Implanted in Human Cortex for Brain-Computer-Interfaces

“…Substantial undergrowth of meningeal tissues can result in displacement of the electrode sites or complete ejection of the device from the CNS ( Woolley et al, 2013 ). Subsequent device ejection is the most prevalent cause of chronic device failure in non-human primates, accounting for nearly 30% of chronic failure ( Barrese et al, 2016 ; Dunlap et al, 2020 ). Longer experimental times increase the chance of meningeal undergrowth and eventual ejection of the recording device from the host tissues ( Rousche and Normann, 1998 ; Barrese et al, 2016 ; Degenhart et al, 2016 ).…”

Explant analysis of Utah electrode arrays implanted in human cortex for brain-computer-interfaces

“…Substantial undergrowth of meningeal tissues can result in displacement of the electrode sites or complete ejection of the device from the CNS. (Woolley et al 2013) Subsequent device ejection is the most prevalent cause of chronic device failure in non-human primates, accounting for nearly 30% of chronic failure (Barrese et al 2016; Dunlap et al 2020) Longer experimental times increase the chance of meningeal undergrowth and eventual ejection of the recording device from the host tissues (Barrese et al 2016; Degenhart et al 2016; Rousche and Normann 1998).…”

Increasing Robustness of Brain–Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals

“…Neural sensors are vulnerable to damage over time ( Barrese et al, 2013 ; Dunlap et al, 2020 ; Colachis et al, 2021 ), and this vulnerability will increase as BCIs become used in more unpredictable environments outside the laboratory. Identifying and adapting to these disruptions will thus be essential in a deployed BCI system.…”

“…Four primary metrics based on both channel impedances and voltage recordings were calculated and monitored to detect signal disruptions. These metrics were selected based on their perceived association with possible types of biological, mechanical, and material damage to the MEA (see Dunlap et al, 2020 ) and their common usage in BCI applications. We note that the SPC approach could be used to monitor any number of other BCI metrics, although we believe the four presented here should be sufficient to identify most significant disruptions.…”

“…Despite significant progress in intracortical brain computer interface (BCI) technology, there are few examples of practical use in home environments ( Collinger et al, 2013b ; Murphy et al, 2016 ; Oxley et al, 2020 ; Weiss et al, 2020 ; Cajigas et al, 2021 ). One obstacle to translation is that chronic BCI systems are likely to encounter signal disruptions due to biological, material, and mechanical issues that can corrupt the neural data ( Barrese et al, 2013 ; Dunlap et al, 2020 ). While some disruptions can be catastrophic and cause complete signal loss, disruptions often only affect a subset of BCI recording channels ( Colachis et al, 2021 ).…”