Cunxin Li

About

Cunxin Li is a leading researcher in the field of neural signal processing and human-robot interaction, with a focus on lower limb exoskeleton control. Their work centers on decoding human voluntary movement intention from electroencephalogram (EEG) and surface electromyogram (sEMG) signals, aiming to create more intuitive and responsive robotic systems. Li’s most cited paper (2022, 12 citations) introduces a novel EEG-sEMG fusion method using a convolutional neural network-long short-term memory (CNN-LSTM) model, achieving precise detection of lower limb movements by addressing the unclear internal relationship between these two signal types. This work has been influential in advancing human-robot collaboration. More recently, Li proposed a cross-domain prediction approach (2024, 4 citations) that leverages the readiness potential (RP) component of motion-related cortical potentials from EEG signals to predict voluntary movement intention before it occurs, enabling proactive exoskeleton control. These contributions are critical for developing assistive technologies for individuals with mobility impairments. Li’s research demonstrates a strong commitment to bridging neural and robotic systems, with potential applications in rehabilitation and augmentative devices.

Research Focus

Key Achievements

Top Papers

1. 1
   Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model

   12 citations · 2022
2. 2
   Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals

   4 citations · 2024

Key Collaborators

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