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.

Configuration of electrical spinal cord stimulation through real-time processing of gait kinematics

“…Gait events can also be detected from brain signals that encode movement. A range of different electrophysiological techniques can be used to decode gait parameters, including non-invasive EEG recordings [50][51][52][53] , electrocorticographic (ECoG) signals recorded using electrodes positioned on the cortical surface 25,54,55 , and local field potentials and action potentials of neuronal ensembles recorded using electrodes implanted into the cortex 8,56,57 . Generally, the decoding accuracy and robustness increase with the increasing spatial resolution .…”

An Established Early Rehabilitation Therapy Demonstrating Higher Efficacy and Safety for Care of Intensive Care Unit Patients

“…The early rehabilitation therapy strategies, including awaking therapy (such as transcranial direct current stimulation (tDCS) [15,16], hyperbaric oxygen therapy [17], comprehensive sensory stimulation therapy [18], and fastigial nucleus stimulation [19]), therapeutic exercise (such as intelligent rehabilitation training system for lower limbs [20], passive activity training/active assistant activity training [21], and electrical stimulation therapy [22] were conducted in this study according to the above documents. Rehabilitation therapy was stopped when we encountered the following conditions: 1) heart rate increased by more than 30% at resting position, heart rate less than 30 beats per minute or more than 130 beats per minute; 2) hypertension increased by more than 20% in quiet recumbent position, and mean arterial pressure (MAP) less than 60 mmHg or more than 110 mmHg; 3) respiration less than 5 breaths per minute or more than 40 breaths per minute, and SpO 2 lower than 90% with supplemental oxygen; 4) body temperature more than 38.5°C or less than 36°C; 5) symptoms of decreased consciousness, sweating, abnormal complexion, and pain; 6) patient did not cooperate with the treatments.…”

Odor Recognition with a Spiking Neural Network for Bioelectronic Nose

“…Dong et al used population vector similarity and support vector machine (SVM) for low concentration odor detection [ 12 ]. Ma et al used the Kalman filter to decode the spike trains of neurons in the primary motor cortex to predict lower limb muscle activity [ 18 ]. The summary of these approaches is shown in Table 1 .…”