Kevin L. Bodkin

Patterned wireless transcranial optogenetics generates artificial perception

This paper develops a wireless transcranial optogenetic system to generate artificial perceptions in mice through controlled cortical stimulation and demonstrates its effectiveness in operant learning tasks.

Stabilizing brain-computer interfaces through alignment of latent dynamics

This paper introduces NoMAD, a method that stabilizes brain-computer interfaces by aligning latent dynamics, achieving stable decoding without supervised recalibration over long periods.

Less is more: selection from a small set of options improves BCI velocity control

This paper introduces a discrete direction selection decoder for iBCI that outperforms continuous velocity decoders in human and monkey experiments by improving control accuracy and reducing noise.

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface

This paper develops an unsupervised piecewise linear decoder for multi-task brain-computer interfaces, showing it outperforms global decoders in EMG prediction.

BRAND: A platform for closed-loop experiments with deep network models

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

Monkey-to-human transfer of brain computer interface decoders

This paper transfers brain-computer interface decoders from monkeys to humans, showing that motor-related neural representations are consistent across species and reducing the need for extensive data.

Using adversarial networks to extend brain computer interface decoding accuracy over time

This paper improves brain-computer interface decoding accuracy over time using Cycle-GAN and ADAN methods, outperforming linear methods with minimal data requirements.