Long term, stable brain machine interface performance using local field potentials and multiunit spikes.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Technology

Background:

• Brain machine interfaces (BMIs) aim to restore movement for individuals with paralysis.
• Current BMIs often require frequent retraining due to performance degradation over time, limiting clinical viability.
• Alternative neural signals like local field potentials (LFPs) and multi-unit spikes (MSPs) may offer improved long-term stability compared to single-unit spikes.

Purpose of the Study:

• To demonstrate the efficacy of LFPs in a biomimetic BMI for computer cursor control.
• To assess the long-term performance and stability of BMIs utilizing LFPs and MSPs without decoder retraining.
• To compare the stability of online BMI control with offline neural signal-movement decoding.

Main Methods:

• Intracortical microelectrodes were implanted in the primary motor cortex of two rhesus macaques.
• Local field potential (LFP) and multi-unit spike (MSP) signals were recorded during a continuous reaching task.
• Biomimetic decoders were constructed using LFP and MSP signals for cursor control.

Main Results:

• High-performance, continuous cursor control was achieved using LFP decoders, remaining stable or improving for nearly 12 months without retraining.
• BMIs controlled with MSPs also demonstrated stable performance for over six months without adaptation.
• Despite stable online control, both LFP and MSP signals exhibited significant variability when used offline to predict hand movements.

Conclusions:

• Monkeys demonstrated the ability to stabilize the neural activity-to-cursor movement relationship during online BMI control.
• This stabilization occurred even when the relationship between neural activity and actual hand movements showed considerable variability.
• LFPs and MSPs show promise for developing robust, long-term brain machine interfaces.