Biology subjects

Ganesh Kumar, N.

Publications and source records attributed to Ganesh Kumar, N..

4 recordsLinked to original sources

bioRxiv2024

Sparse neural networks enable low-power, implantable neural interfaces

Recent advances in brain-machine interfaces (BMIs) using neural network decoders and increased channel count have improved the restoration of speech and motor function, but at the cost of higher power consumption. For wireless, implantable BMIs to be clinically viable, power consumption must be limited to prevent thermal tissue damage and enable long use without frequent charging. Here, we show how neural network "pruning" creates sparse decoders that require fewer computations and active channels for reduced power consumption. Across multiple movement decoding tasks using brain and muscle signals, recurrent neural network decoders can be compressed by over 100x while maintaining strong performance, enabling decoding on the implant with <1% power increase compared to decoding externally. Pruning also allows for deactivating up to 89% of channels, reducing BMI power by up to 5x. Counterintuitively, our findings suggest that BMIs employing a subset of a larger number of channels may achieve lower power consumption than BMIs with fewer channels, for a given performance level. These results suggest a path toward power-efficient, implantable BMIs suitable for long-term clinical use.

bioengineering↗

bioRxiv2024

Functional Electrical Stimulation and Brain-Machine Interfaces for Simultaneous Control of Wrist and Finger Flexion

Brain-machine interface (BMI) controlled functional electrical stimulation (FES) is a promising treatment to restore hand movements to people with cervical spinal cord injury. Recent intracortical BMIs have shown unprecedented successes in decoding user intentions, however the hand movements restored by FES have largely been limited to predetermined grasps. Restoring dexterous hand movements will require continuous control of many biomechanically linked degrees-of-freedom in the hand, such as wrist and finger flexion, that would form the basis of those movements. Here we investigate the ability to restore simultaneous wrist and finger flexion, which would enable grasping with a controlled hand posture and assist in manipulating objects once grasped. We demonstrate that intramuscular FES can enable monkeys with temporarily paralyzed hands to move their fingers and wrist across a functional range of motion, spanning an average 88.6 degrees at the metacarpophalangeal joint flexion and 71.3 degrees of wrist flexion, and intramuscular FES can control both joints simultaneously in a real-time task. Additionally, we demonstrate a monkey using an intracortical BMI to control the wrist and finger flexion in a virtual hand, both before and after the hand is temporarily paralyzed, even achieving success rates and acquisition times equivalent to able-bodied control with BMI control after temporary paralysis in two sessions. Together, this outlines a method using an artificial brain-to-body interface that could restore continuous wrist and finger movements after spinal cord injury. One Sentence SummaryWe show in non-human primates that intramuscular functional electrical stimulation can flex the wrist and fingers through a functional range of movement, can be controlled precisely, and that a brain-machine interface can determine command signals for intended finger and wrist flexion.

neuroscience↗

bioRxiv2022

The Impact of Task Context on Predicting Finger Movements in a Brain-Machine Interface

A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patients own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that predicting finger kinematics and finger-related muscle activations across contexts led to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either training task context or the hands physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts, possibly explaining biased kinematic predictions and suggesting a feature that could help predict different magnitude muscle activations while producing similar kinematics.

neuroscience↗

bioRxiv2022

Brain-Controlled Electrical Stimulation Restores Continuous Finger Function

Brain-machine interfaces have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a users own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand. In a one-dimensional, continuous, finger-related target acquisition task, the monkey improved his success rate to 83% (1.5s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5s median acquisition, equivalent to chance) when attempting to use his temporarily paralyzed hand. With two monkeys under general anesthesia, we found FES alone could control the monkeys fingers to rapidly reach targets in a median 1.1s but caused oscillation about the target. Finally, when attempting to perform a virtual two-finger continuous target acquisition task in brain-control mode following temporary hand paralysis, we found performance could be completely recovered by executing recalibrated feedback-intention training one time following temporary paralysis. These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting performance factor in a BCFES neuroprosthesis.

neuroscience↗