2 citations · 4 across the 5 of their papers we have counts for
5 papers
Pseudo Channel: Time Embedding for Motor Imagery Decoding
Zhengqing Miao, Meirong Zhao
Motor imagery (MI) based EEG represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation. This study introduces a novel time emb…
Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG Classification
Zhengqing Miao, Meirong Zhao
In the realm of EEG decoding, enhancing the performance of artificial neural networks (ANNs) carries significant potential. This study introduces a novel approach, termed "weight f…
Time-space-frequency feature Fusion for 3-channel motor imagery classification
Zhengqing Miao, Meirong Zhao
Low-channel EEG devices are crucial for portable and entertainment applications. However, the low spatial resolution of EEG presents challenges in decoding low-channel motor imager…
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
Zhengqing Miao, Xin Zhang, Meirong Zhao +1
EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neu…
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Zhengqing Miao, Xin Zhang, Carlo Menon +3
Motor imagery (MI) is a common brain computer interface (BCI) paradigm. EEG is non-stationary with low signal-to-noise, classifying motor imagery tasks of the same participant from…