Closed-loop cortical control of virtual reach and posture using Cartesian and joint velocity commands.

OBJECTIVE: Brain-computer interfaces (BCIs) are a promising technology for the restoration of function to people with paralysis, especially for controlling coordinated reaching. Typical BCI studies decode Cartesian endpoint velocities as commands, but human arm movements might be better controlled in a joint-based coordinate frame, which may match underlying movement encoding in the motor cortex. A better understanding of BCI controlled reaching by people with paralysis may lead to performance improvements in brain-controlled assistive devices. APPROACH: Two intracortical BCI participants in the BrainGate2 pilot clinical trial performed a visual 3D endpoint virtual reality reaching task using two decoders: Cartesian and joint velocity. Task performance metrics (i.e. success rate and path efficiency) and single feature and population tuning were compared across the two decoder conditions. The participants also demonstrated the first BCI control of a fourth dimension of reaching, the arm's swivel angle, in a 4D posture matching task. MAIN RESULTS: Both users achieved significantly higher success rates using Cartesian velocity control, and joint controlled trajectories were more variable and significantly more curved. Neural tuning analyses showed that most single feature activity was best described by a Cartesian kinematic encoding model, and population analyses revealed only slight differences in aggregate activity between the decoder conditions. Simulations of a BCI user reproduced trajectory features seen during closed-loop joint control when assuming only Cartesian-tuned features passed through a joint decoder. With minimal training, both participants controlled the virtual arm's swivel angle to complete a 4D posture matching task, and achieved significantly higher success using a Cartesian + swivel velocity decoder compared to a joint velocity decoder. SIGNIFICANCE: These results suggest that Cartesian velocity command interfaces may provide better BCI control of arm movements than other kinematic variables, even in 4D posture tasks with swivel angle targets.

Signal processing methods for reducing artifacts in microelectrode brain recordings caused by functional electrical stimulation.

OBJECTIVE: Functional electrical stimulation (FES) is a promising technology for restoring movement to paralyzed limbs. Intracortical brain-computer interfaces (iBCIs) have enabled intuitive control over virtual and robotic movements, and more recently over upper extremity FES neuroprostheses. However, electrical stimulation of muscles creates artifacts in intracortical microelectrode recordings that could degrade iBCI performance. Here, we investigate methods for reducing the cortically recorded artifacts that result from peripheral electrical stimulation. APPROACH: One participant in the BrainGate2 pilot clinical trial had two intracortical microelectrode arrays placed in the motor cortex, and thirty-six stimulating intramuscular electrodes placed in the muscles of the contralateral limb. We characterized intracortically recorded electrical artifacts during both intramuscular and surface stimulation. We compared the performance of three artifact reduction methods: blanking, common average reference (CAR) and linear regression reference (LRR), which creates channel-specific reference signals, composed of weighted sums of other channels. MAIN RESULTS: Electrical artifacts resulting from surface stimulation were 175 × larger than baseline neural recordings (which were 110 µV peak-to-peak), while intramuscular stimulation artifacts were only 4 × larger. The artifact waveforms were highly consistent across electrodes within each array. Application of LRR reduced artifact magnitudes to less than 10 µV and largely preserved the original neural feature values used for decoding. Unmitigated stimulation artifacts decreased iBCI decoding performance, but performance was almost completely recovered using LRR, which outperformed CAR and blanking and extracted useful neural information during stimulation artifact periods. SIGNIFICANCE: The LRR method was effective at reducing electrical artifacts resulting from both intramuscular and surface FES, and almost completely restored iBCI decoding performance (>90% recovery for surface stimulation and full recovery for intramuscular stimulation). The results demonstrate that FES-induced artifacts can be easily mitigated in FES + iBCI systems by using LRR for artifact reduction, and suggest that the LRR method may also be useful in other noise reduction applications.

Seasonal population dynamics of the potato psyllid (Hemiptera: Triozidae) and its associated pathogen "Candidatus Liberibacter solanacearum" in potatoes in the southern great plains of North America.

The potato psyllid, Bactericera cockerelli (Sulc) (Hemiptera: Triozidae), and its associated pathogen "Candidatus Liberibacter solanacearum" (Ca. L. solanacearum), the putative causal agent of zebra chip (ZC) disease in potatoes (Solanum tuberosum L.), were sampled in commercial potato fields and untreated control plots for 3 yr in multiple locations in Texas, Kansas, Nebraska, and Colorado. Populations of the potato psyllid varied across years and across potato growing regions. However, the percentage of potato psyllids infected with Ca. L. solanacearum although variable across years, was consistently highest in the Lower Rio Grande Valley of Texas (LRGV), the reported overwintering location for this pest. The numbers of Ca. L. solanacearum-infected psyllids collected on field traps and large nymphs counted on leaf samples were both positively correlated with the final percentage of ZC in tubers. In the LRGV, where vector and disease pressure is the highest, population levels of immature life stages of the psyllid and percentage of ZC differed greatly between commercial and untreated fields. These results show that the pest management program that was used can be effective at controlling development of the psyllid and ultimately reducing the incidence of ZC.