Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements

abstract: Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed algorithms. Our findings provide new insights for extending current BMI design concepts and techniques on upper limbs to lower limb circumstances. Brain controlled exoskeleton, prostheses or neuromuscular electrical stimulators for lower limbs are expected to be developed, which enables the subject to manipulate complex biomechatronic devices with mind in more harmonized manner.View the article as published at http://journal.frontiersin.org/article/10.3389/fnins.2017.00044/ful

Successfully Controlled BCI Through Minimal Dry Electrodes

ABSTRACT: There are approximately 185,000 amputations a year in the United States according to the Amputee Coalition with the number of amputations going up. While it is common for someone with a lower limb amputation to use a prosthetic, approximately 84%, it is not as common for people with upper limb amputations, approximately 56% (Raichle et al., 2008). The time it takes an amputee to get a prosthetic affects the likelihood of use, in addition to functionality (Miller et al., 2020). The purpose of this project is to show proof of concept of an EEG-controlled prosthetic, using only 2 dry-electrodes, through the use of BCI2000 using imagined movements. Eight (N-8) participants were recruited to complete a pre-training mu task, a 1D cursor training task, a 2D cursor training task, and the main 2D cursor task. After a frequency was established for each participant, they completed 200 trials of the 1D cursor task for three different conditions (left, right, and both hand(s)) or reached a success rate of 80% for 4 trials in a row with random targets. The participants then completed the 2D cursor task with random targets until a success rate of 70% for 4 trials in a row was achieved, followed by a 2D cursor task where the targets were pre-determined. A chi-squared test determined the goodness of fit for the success rate was significant (p < 0.001) for all participants completing the 1D cursor task. The combined success rate for the participants during task 1 for their right hand was 30.16%, 47.11% for their left hand, and 61.47% for both hands. The combined success rate for task 2 was 69.40% and 79.59% for the main task. Overall, this study successfully showed that 2 dry electrodes can be used to detect imagined movements through BCI. While the accuracy can still be improved, by enhancing the equipment and developing the training protocol, both participants that completed the main task were able to surpass the expected overall accuracy and surpass 4 out of the 6 individual accuracies. Whether it is to control a mechanical arm, leg, or other body part, the framework of this study grants development opportunities for BCI from a few dry electrodes