Brain-Computer Interface

Human motor cortex encodes complex handwriting through a sequence of stable neural states
Human motor cortex encodes complex handwriting through a sequence of stable neural states

Nat Hum Behav | The human motor cortex encodes complex handwriting by transitioning through neural states, each with distinct directional tuning, enabling accurate reconstruction of written characters.

Apr 2, 2025

Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals
Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals

eLife | A framework is proposed to separate and analyze behaviorally relevant neural signals, revealing that previously overlooked neural responses encode rich information and suggesting that motor behaviors occupy a higher-dimensional space than expected and can be decoded linearly.

Aug 9, 2024

Speed-enhanced Subdomain Alignment for Long-term Stable Neural Decoding in Brain-computer Interfaces
Speed-enhanced Subdomain Alignment for Long-term Stable Neural Decoding in Brain-computer Interfaces

BIBM | This paper proposes SeSA, a semi-supervised domain adaptation framework that improves BCI recalibration by aligning both global and speed-specific neural features, achieving robust decoding across days despite neural nonstationarities.

Jan 1, 2024

Tracking functional changes in nonstationary signals with evolutionary ensemble bayesian model for robust neural decoding
Tracking functional changes in nonstationary signals with evolutionary ensemble bayesian model for robust neural decoding

NeurIPS | A novel evolutionary ensemble framework, EvoEnsemble, dynamically adapts to changes in neural signals by evolving decoders over time, significantly improving the accuracy and robustness of neural decoding in nonstationary conditions.

Dec 6, 2022

A brain-controlled mahjong game with artificial intelligence augmentation
A brain-controlled mahjong game with artificial intelligence augmentation

CICAI | An AI-augmented brain-computer interface is developed for a mahjong game, enabling easier and more precise control by allowing users to select from AI-suggested options, thus improving usability and user experience in a real BCI application.

Aug 27, 2022

Dynamic ensemble bayesian filter for robust control of a human brain-machine interface
Dynamic ensemble bayesian filter for robust control of a human brain-machine interface

IEEE Trans on Biomedical Engineering | A dynamic ensemble Bayesian filter (DyEnsemble) is proposed to improve the robustness of online brain-machine interface (BMI) control by adaptively combining multiple neural decoding models to handle neural signal variability, showing significantly better accuracy and stability than conventional methods.

Jun 13, 2022