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

For brain-computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.

Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis.

Objective. Brain-computer interfaces (BCIs) that record neural activity using intracortical microelectrode arrays (MEAs) have shown promise for mitigating disability associated with neurological injuries and disorders. While the chronic performance and failure modes of MEAs have been well studied and systematically described in non-human primates, there is far less reported about long-term MEA performance in humans. Our group has collected one of the largest neural recording datasets from a Utah MEA in a human subject, spanning over 5 years (2014-2019). Here we present both long-term signal quality and BCI performance as well as highlight several acute signal disruption events observed during the clinical study.Approach. Long-term Utah array performance was evaluated by analyzing neural signal metric trends and decoding accuracy for tasks regularly performed across 448 clinical recording sessions. For acute signal disruptions, we identify or hypothesize the root cause of the disruption, show how the disruption manifests in the collected data, and discuss potential identification and mitigation strategies for the disruption.Main results. Neural signal quality metrics deteriorated rapidly within the first year, followed by a slower decline through the remainder of the study. Nevertheless, BCI performance remained high 5 years after implantation, which is encouraging for the translational potential of this technology as an assistive device. We also present examples of unanticipated signal disruptions during chronic MEA use, which are critical to detect as BCI technology progresses toward home usage.Significance. Our work fills a gap in knowledge around long-term MEA performance in humans, providing longevity and efficacy data points to help characterize the performance of implantable neural sensors in a human population. The trial was registered on ClinicalTrials.gov (Identifier NCT01997125) and conformed to institutional requirements for the conduct of human subjects research.

Natural history of disease in cynomolgus monkeys exposed to Ebola virus Kikwit strain demonstrates the reliability of this non-human primate model for Ebola virus disease.

Filoviruses (Family Filoviridae genera Ebolavirus and Marburgvirus) are negative-stranded RNA viruses that cause severe health effects in humans and non-human primates, including death. Except in outbreak settings, vaccines and other medical countermeasures against Ebola virus (EBOV) will require testing under the FDA Animal Rule. Multiple vaccine candidates have been evaluated using cynomolgus monkeys (CM) exposed to EBOV Kikwit strain. To the best of our knowledge, however, animal model development data supporting the use of CM in vaccine research have not been submitted to the FDA. This study describes a large CM database (122 CM, 62 female and 60 male, age 2 to 9 years) and demonstrates the consistency of the CM model through time to death models and descriptive statistics. CMs were exposed to EBOV doses of 0.1 to 100,000 PFU in 33 studies conducted at three Animal Biosafety Level 4 facilities, by three exposure routes. Time to death was modeled using Cox proportional hazards models with a frailty term that incorporated study-to-study variability. Despite significant differences attributed to exposure variables, all CMs exposed to the 100 to 1,000 pfu doses commonly used in vaccine studies died or met euthanasia criteria within 21 days of exposure, median 7 days, 93% between 5 and 12 days of exposure. Moderate clinical signs were observed 4 to 5 days after exposure and preceded death or euthanasia by approximately one day. Viremia was detected within a few days of infection. Hematology indices were indicative of viremia and the propensity for hemorrhage with progression of Ebola viremia. Changes associated with coagulation parameters and platelets were consistent with coagulation disruption. Changes in leukocyte profiles were indicative of an acute inflammatory response. Increased liver enzymes were observed shortly after exposure. Taken together, these factors suggest that the cynomolgus monkey is a reliable animal model for human disease.