Junfeng Lu

This paper investigates the neural coding of articulatory dynamics during speech imagery and production, revealing a hierarchical spatiotemporal organization and developing a model to decode speech from neural signals.

This study demonstrates high-density single-unit recordings in human cortex using uFINE arrays during surgery, achieving stable recordings of up to 135 units from 10 patients.

This paper develops a brain-to-text framework for decoding natural Mandarin tonal sentences from neural recordings, achieving higher accuracy than previous methods.

This study uses deep neural networks to model neural coding in the human auditory pathway and finds that DNN representations align with biological auditory processing.

The paper identifies four low-dimensional states of resting-state language networks, showing domain-separated dynamics and cognitive relevance.