Topic: multi-effector movement

Advancements in machine learning and artificial intelligence have given rise to promising neural network-based models of the complex nonlinear relationships between movement intention and neural activity. In particular, recurrent neural networks (RNNs) are one commonly used algorithm for brain-computer interface (BCI) control, due to their ability to learn temporal dependencies within the training data. However, most BCI training data contains highly stereotyped temporal and behavioral structure to which RNNs can overfit, causing poor performance in online settings when behavior and temporal c