Samuel R. Nason

This paper introduces a multi-user transformer-based decoder for intracortical speech BCIs, achieving lower word error rates with less training data than individual models.

This paper tests deep neural ensembles in real-time speech BCIs, showing reduced word error rates and introducing a computationally efficient pseudoensembling method.

This paper demonstrates cross-brain transfer learning for intracortical BCIs decoding speech and handwriting, showing improved performance with limited training data and specific decoder design.

This study investigates how preparatory activity in the human motor cortex encodes movement features like direction, curvature, and distance, and demonstrates its use for predictive brain-computer interface control.

The paper studies neural encoding of inner speech in motor cortex and shows it can be decoded, but also identifies a neural dimension that can prevent unintentional decoding of private thoughts.

The paper maps human motor cortex using microelectrode recordings, finding whole-body representations and challenging the traditional motor homunculus model.

BRAND provides a platform for closed-loop experiments with deep networks, achieving low latency and supporting multiple programming languages.

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.