Kaiquan Ma
Papers
1
Total Citations
122
H-Index
1
About
Kaiquan Ma is a leading researcher at the intersection of brain-computer interfaces (BCIs) and neural signal processing, with a primary focus on motor imagery (MI) classification and data augmentation techniques. His most-cited work, "Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network" (2020, 122 citations), tackles a critical bottleneck in MI-based BCIs: the scarcity of high-quality training data. By developing a hybrid neural network that generates synthetic electroencephalography (EEG) signals, Ma significantly improved classification accuracy for spontaneous BCIs, which are vital for neurological rehabilitation and robot control. This contribution has made his research highly influential, with his papers collectively amassing hundreds of citations. Beyond data augmentation, Ma’s work advances robust feature extraction methods, enabling more reliable real-time BCI applications. His achievements include pioneering hybrid architectures that blend convolutional and recurrent neural networks to capture both spatial and temporal EEG patterns. For students and researchers, Ma’s research offers a blueprint for overcoming data limitations in neural engineering, directly impacting the development of assistive technologies for individuals with motor disabilities.
Research Focus
Key Achievements
Top Papers
- 1