Matthew S. Willsey

This study demonstrates that intramuscular FES can restore wrist and finger flexion in paralyzed monkeys, controlled by a BMI, achieving functional movement ranges and performance comparable to able-bodied control.

This study evaluates if brain and muscle synergies improve decoding performance in implanted brain-machine interfaces (iBMIs) using dimensionality reduction techniques, finding that they do not enhance performance or generalization.

This paper develops a high-performance brain-computer interface for finger control and quadcopter navigation, achieving fast target acquisition and obstacle course performance.

This paper shows that sparse neural networks reduce power consumption in implantable BMIs by pruning decoders, enabling efficient decoding with minimal performance loss.

This paper develops a flexible intracortical BCI for typing using finger movements, achieving high accuracy and speed in two typing paradigms.

This study shows that artificial neural networks produce more naturalistic finger movements than linear methods in brain-machine interfaces.

This study compares KalmanNet, an explainable BMI decoder, with deep-learning and Kalman filter models, finding it achieves comparable performance while offering interpretability.

This study examines how task context affects brain-machine interface performance, finding that offline predictions are sensitive to context changes but online control remains stable.

This paper develops a biologically-inspired neural network decoder for real-time brain-machine interfaces, achieving faster and more natural prosthetic finger movements than existing methods.

This study demonstrates that a brain-controlled functional electrical stimulation system can restore continuous finger function in a paralyzed monkey, achieving high success rates with minimal training.

This study investigates the effects of zona incerta stimulation on human pain perception and finds it reduces heat pain but not other sensations.