公开(公告)号：US20250072812A1

公开(公告)日：2025-03-06

申请号：US18808099

申请日：2024-08-19

Applicant: SOUTHEAST UNIVERSITY

Inventor： Baoguo XU ,  Zelin GAO ,  Xinhao YANG ,  Aiguo SONG

IPC: A61B5/374 ,  A61B5/00

Abstract: Disclosed is an EEG recognition method for a natural hand movement based on a time-domain and frequency-domain multi-layer brain network, including: (1) acquiring a multi-channel EEG signal of the natural hand movement; (2) preprocessing the multi-channel EEG signal, and extracting a δ wave, a θ wave, a α wave, a β wave, and a γ wave at each time point; (3) constructing a time-domain multi-layer brain network using a wSAR model; (4) calculating the frequency-domain multi-layer brain network using the phase-amplitude coupling; (5) combining the time-domain multi-layer brain network and the frequency-domain multi-layer brain network, and performing standardization; and (6) calculating metrics of the time-domain and frequency-domain multi-layer brain network and a super-adjacency matrix of a decomposed time-domain and frequency-domain multi-layer brain network, inputting the same to a two-layer graph convolutional network (GCN), and fusing manual, shallow, and deep features for analysis.

公开(公告)号：US20250077837A1

公开(公告)日：2025-03-06

申请号：US18800117

申请日：2024-08-11

Applicant: SOUTHEAST UNIVERSITY

Inventor： Baoguo XU ,  Xinhao YANG ,  Zelin GAO ,  Aiguo SONG

IPC: G06N3/0442 ,  G06F3/01

Abstract: Disclosed is an online estimation method for wrist torque based on neural features and LSTM, including following steps: (1) an experimenter keeps his/her arms stationary and applies torque to a torque sensor through his/her wrist; (2) acquiring data from the torque sensor and high-density surface EMG ((HD-sEMG) synchronously; (3) decomposing the HD-sEMG using a blind source separation algorithm to obtain a motor unit spike train (MUST); (4) constructing input and output vectors on the basis of the original HD-sEMG and the decomposed MUST, performing training of the LSTM, and performing polynomial regression of a discharge rate and torque of a neural feature; and (5) calculating a real-time discharge rate (DR) of the CST using a sliding window approach for real-time estimation of the torque.