公开(公告)号：US20230095702A1

公开(公告)日：2023-03-30

申请号：US17874552

申请日：2022-07-27

Applicant: ZHEJIANG LAB

Inventor： Tao TANG

IPC: A61B5/31

Abstract: The present application discloses a method for eliminating multi-channel gain errors of an EEG signal acquisition system. The system includes an acquisition electrode, decoupling input capacitors, input units, intra-channel chopper modulators, inter-channel chopper modulators, fully differential closed-loop amplifiers, inter-channel chopper demodulators, intra-channel chopper demodulators, low-pass filters and an output unit sequentially arranged in a signal flow direction of channels. The method includes a process of intra-channel chopping modulation, multi-level inter-channel chopping modulation, amplification, multi-level inter-channel chopping demodulation, intra-channel chopping demodulation, and low-pass filtering. The present application eliminates gain errors between channels, ensures signal acquisition accuracy, and avoids flicker noise in amplifiers.

公开(公告)号：US20230315203A1

公开(公告)日：2023-10-05

申请号：US18115678

申请日：2023-02-28

Applicant: ZHEJIANG LAB

Inventor： Yina WEI ,  Lijie WANG ,  Jinbiao LIU ,  Tao TANG ,  Linqing FENG ,  Zhengting CAI

IPC: G06F3/01 ,  A61B5/00 ,  A61B5/378

CPC classification number: G06F3/015 ,  A61B5/7264 ,  A61B5/378

Abstract: The present disclosure discloses a brain-computer interface decoding method and apparatus based on point-position equivalent augmentation. According to the method, a point-position equivalent transformation is performed on sampling points to augment training data and generate arrangement sets. The task-related component analysis is performed on the augmented data to generate spatial filter. Afterwards, a full-frequency directed rearrangement is performed on verification signals or test signals according to the equivalent arrangement sets. After spatial filtering, Pearson correlation coefficients between the rearranged signals and the decoding templates are calculated. These correlation coefficients will be classified and voted by using a naive Bayes method. The verification module will generate the coefficient probability density functions and a threshold, and the test module will finally output the predicted label based on these information.