A5026994207- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Blind Source Separation Techniques
Chongqing University of Posts and Telecommunications
2021
Deep learning technology is rapidly spreading in recent years and has been extensive attempts the field of Brain-Computer Interface (BCI).Though accuracy Motor Imagery (MI) BCI systems based on deep have greatly improved compared with some traditional algorithms, it still a big problem to clearly interpret models.To address issues, this work first introduces popular model EEGNet compares algorithm Filter-Bank Common Spatial Pattern (FBCSP).After that, considers that 1-D convolution can be...
The effective features of the motor imagery (MI) electroencephalogram (EEG) signals plays a significant role to improve classification accuracy for brain-computer interface (BCI) system. Some traditional methods usually extract frequency or spatial without considering related information between different channels that would affect performance. This paper proposes new method feature extraction EEG based on fusion time-frequency and features. At beginning, common pattern (CSP) algorithm is...