Zhongtao Chen

Du-IN-v2: Unleashing the Power of Vector Quantization for Decoding Cognitive States from Intracranial Neural Signals

The paper introduces Du-IN-v2, a pre-training framework for speech decoding from intracranial neural signals, achieving state-of-the-art performance using region-level token embeddings and discrete codex-guided mask modeling.

Interpreting Sleep Activity Through Neural Contrastive Learning

The paper develops a neural network model, Sleep Interpreter (SI), to decode memory replay from sleep EEG and shows it can accurately detect reactivation during sleep that correlates with memory performance.