Foram B. Kamdar

The paper shows that premotor cortex uses a compositional neural geometry to plan words by combining phoneme representations, enabling efficient planning of novel sequences.

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 paper improves the interpretability of LFADS models by introducing a per-trial bias, enabling simpler contextual dynamics and better analysis of neural activity in simulated and real data.

This study analyzes neural activity in the precentral gyrus of two spinal cord injury patients to classify hand gestures with 70% accuracy, showing potential for intracortical BCI applications.

The paper shows error detection signals in speech motor cortex during neuroprosthesis use, enabling error identification with 86% accuracy and revealing distinct patterns for different error types.

The paper shows that neural activity in human premotor cortex combines finger movements through pseudo-linear summation, with deviations that improve decoding performance.

This paper develops a high-performance brain-computer interface for finger control and quadcopter navigation, achieving fast target acquisition and obstacle course performance.

The paper measures neural signal instability in long-term intracortical recordings to improve brain-computer interface performance through recalibration timing.

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.

This paper develops a flexible intracortical BCI for typing using finger movements, achieving high accuracy and speed in two typing paradigms.

This paper demonstrates a high-performance speech neuroprosthesis that achieves low word error rates and high decoding speeds using intracortical recordings from a person with ALS.

This paper introduces a hidden Markov model for unsupervised recalibration of intracortical brain-computer interfaces, showing improved performance over long-term use.