Matthew J. Mender

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 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.

The paper introduces FALCON, a benchmark suite for evaluating robustness of iBCI decoders using standardized datasets and evaluation metrics.

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.