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, 10 (5), 056005

Long Term, Stable Brain Machine Interface Performance Using Local Field Potentials and Multiunit Spikes

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Long Term, Stable Brain Machine Interface Performance Using Local Field Potentials and Multiunit Spikes

Robert D Flint et al. J Neural Eng.

Abstract

Objective: Brain machine interfaces (BMIs) have the potential to restore movement to people with paralysis. However, a clinically-viable BMI must enable consistently accurate control over time spans ranging from years to decades, which has not yet been demonstrated. Most BMIs that use single-unit spikes as inputs will experience degraded performance over time without frequent decoder re-training. Two other signals, local field potentials (LFPs) and multi-unit spikes (MSPs), may offer greater reliability over long periods and better performance stability than single-unit spikes. Here, we demonstrate that LFPs can be used in a biomimetic BMI to control a computer cursor.

Approach: We implanted two rhesus macaques with intracortical microelectrodes in primary motor cortex. We recorded LFP and MSP signals from the monkeys while they performed a continuous reaching task, moving a cursor to randomly-placed targets on a computer screen. We then used the LFP and MSP signals to construct biomimetic decoders for control of the cursor.

Main results: Both monkeys achieved high-performance, continuous control that remained stable or improved over nearly 12 months using an LFP decoder that was not retrained or adapted. In parallel, the monkeys used MSPs to control a BMI without retraining or adaptation and had similar or better performance, and that predominantly remained stable over more than six months. In contrast to their stable online control, both LFP and MSP signals showed substantial variability when used offline to predict hand movements.

Significance: Our results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship between neural activity and hand movements.

Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1
Figure 1
Schematic of hand and brain control tasks. A, Random target pursuit task under hand control. Monkeys moved the cursor (yellow circle) to a series of targets (red squares). B, Monkeys performed brain control using either LFPs (left pathway) or MSPs (right pathway). Only one signal type at a time was used to decode velocity (,) for brain control. LMP, local motor potential (see Methods); R, Pearson’s correlation coefficient. C, Example cursor trajectories under brain control.
Figure 2
Figure 2
Brain control performance trends. A, Success rate. Each circle is the ratio of successful trials to all trials for 1 epoch. The grey dashed lines and shaded areas show the mean and standard deviation of chance performance. B–E, Kinematic performance measures. Time to target, cursor speed, and path length were calculated for each trial; each circle represents the median across trials for 1 epoch. Solid lines through the LFP1 and MSP points are least-squares linear-fit lines.
Figure 3
Figure 3
Brain control performance in early (left bars) vs. late (right bars) epochs. Starred differences were significant at the p<0.0125 level (t-test, Bonferroni correction for multiple comparisons). Error bars represent standard deviation.
Figure 4
Figure 4
Offline hand velocity decoding performance over time using static decoders. A) Decoding performance for a single LFP decoder (left panel) and a single MSP decoder (right panel) from monkey C. Note the day-to-day variability in performance after the first few days. B, C) When averaged across decoders, mean decoding performance declined significantly after day 0 in both (B) monkey C and in (C) monkey M using LFPs and MSPs. Error bars denote standard deviation. For monkey M’s MSP-based decoding (C, right), the change in performance did not become significant until after day 64. In B and C, black error bars indicate significant differences from day 0 (1-way ANOVA for each panel, all main effects were significant (p<0.001), Tukey HSD post-hoc test, significance below p=0.05.
Figure 5
Figure 5
Stability of LFP feature power and MSP firing rates for monkey M. A) Mean values in each epoch of the LFP features included in the fixed LFP1 decoder. B) Mean firing rates in each epoch of the units included in the fixed MSP decoder. We normalized feature values and firing rates to the maximum value for each feature or unit over all epochs, and sorted according to the values in the first epoch.

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