Dylan M Wallace

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

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.