Decoding Joint-Level Hand Movements With Intracortical Neural Signals in a Human Brain–Computer Interface

Fine movements of hands play an important role in everyday life. While existing studies have successfully decoded hand gestures or finger movements from brain signals, direct decoding of single-joint kinematics remains challenging. This study aims to investigate the decoding of fine hand movements at the single-joint level. Neural activities were recorded from the motor cortex (MC) of a human participant while imagining eleven different hand movements. We comprehensively evaluated the decoding efficiency of various brain signal features, neural decoding algorithms, and single-joint kinematic variables for decoding. Results showed that using the spiking band power (SBP) signals, we could faithfully decode the single-joint angles with an average correlation coefficient of 0.77, outperforming other brain signal features. Nonlinear approaches that incorporate temporal context information, particularly recurrent neural networks, significantly outperformed traditional methods. Decoding joint angles yielded superior results compared to joint angular velocities. Our approach facilitates the construction of high-performance brain–computer interfaces for dexterous hand control.