Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces
Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder’s output, where the correction may over-dominate the user’s intention.
Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces
Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications…
Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain Machine Interfaces
We propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multi-channel neural spike trains from the primary motor cortex of a monkey performing a target pursuit task, and test our computational approach with successful tracking on the neural modulation depth over time and improvement on kinematic reconstruction.