The Brain-Inspired Cooperative Shared Control for Brain-Machine Interface

In the practical application of brain-machine interface technology, the problem often faced is the low information content and high noise of the neural signals collected by the electrode and the difficulty of decoding by the decoder, which makes it difficult for the robotic to obtain stable instructions to complete the task. The idea based on the principle of cooperative shared control can be achieved by extracting general motor commands from brain activity, while the fine details of the movement can be hosted to the robot for completion, or the brain can have complete control. This study proposes a brain-machine interface shared control system based on spiking neural networks for robotic arm movement control and wheeled robots wheel speed control and steering, respectively. The former can reliably control the robotic arm to move to the destination position, while the latter controls the wheeled robots for object tracking and map generation. The results show that the shared control based on brain-inspired intelligence can perform some typical tasks in complex environments and positively improve the fluency and ease of use of brain-machine interaction, and also demonstrate the potential of this control method in clinical applications of brain-machine interfaces.

An Adaptive Contrastive Learning Model for Spike Sorting

Brain-computer interfaces (BCIs), is ways for electronic devices to communicate directly with the brain. For most medical-type brain-computer interface tasks, the activity of multiple units of neurons or local field potentials is sufficient for decoding. But for BCIs used in neuroscience research, it is important to separate out the activity of individual neurons. With the development of large-scale silicon technology and the increasing number of probe channels, artificially interpreting and labeling spikes is becoming increasingly impractical. In this paper, we propose a novel modeling framework: Adaptive Contrastive Learning Model that learns representations from spikes through contrastive learning based on the maximizing mutual information loss function as a theoretical basis. Based on the fact that data with similar features share the same labels whether they are multi-classified or binary-classified. With this theoretical support, we simplify the multi-classification problem into multiple binary-classification, improving both the accuracy and the runtime efficiency. Moreover, we also introduce a series of enhancements for the spikes, while solving the problem that the classification effect is affected because of the overlapping spikes.