Nicholas Au Yong

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 study decodes speech mode and loudness from ventral precentral gyrus activity in two participants, achieving high accuracy in predicting speech parameters and developing a closed-loop decoder for expressive speech neuroprostheses.

The paper uses artificial intelligence to analyze spinal interneuron activity and muscle output in cats, revealing how state space trajectories capture timing and magnitude variations in muscle activation across steps.

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.

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

The paper shows that GABAergic synaptic scaling, not AMPAergic scaling, homeostatically controls spiking levels by responding to changes in spiking activity rather than direct receptor activation.