Darrel R. Deo

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.

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

This paper shows that recurrent neural networks (RNNs) can be adapted for real-time neuroprosthetic control by modifying their training data's temporal structure, enabling better performance than standard linear methods.

This study investigates how haptic feedback affects motor cortical control and iBCI performance in a tetraplegic participant.

The paper investigates the representation of body movements in the motor cortex of tetraplegic individuals, finding a modular code that could improve brain-computer interfaces.