Decoding Gait Speed Using Intracortical Signals in a Freely-Moving Non-Human Primate for the Control of Neurorobotics

Locomotion is one of the most fundamental and crucial motor abilities in both humans and animals. Compared to other quadrupedal mammals, primates exhibit unique quadrupedal characteristics, such as diagonal gait and compliant walking. Significant progress has been made in past studies to understand the relationship between behaviors and neural activities in primates. However, these studies often limit the behavior to constrained environments and focus only on the upper limbs. Here, we trained a macaque to walk freely on a treadmill at four different speeds (0.5, 1.0, 1.5, 2.0 km/h), Intracortical signals and videos were recorded at the same time. Our results indicate that the primary motor cortex (M1) exhibits higher neuronal firing rates during the swing phase of gait, suggesting its critical role in gait encoding. Furthermore, we utilized only eight neural units to decode gait speeds and employed mutual information for feature selection to improve the decoding accuracies. Results indicated that four gait speeds can be reliably classified with an accuracy of 82.1%. The firing rates of neurons from 100 ms to 300 ms before the forelimb raise exhibited the most variations during task execution and contributed the most to the decoder model. This study provides new insights into the cortical control of quadrupedal locomotion. The proposed cortical speed decoder can be applied in brain-computer-interface (BCI) to control neurorobotics such as exoskeletons and prostheses.