Main Image
Volume 299 - The 7th International Conference on Computer Engineering and Networks (CENet2017) - Session I - Machine Learning
Classification of EEG Signal by STFT-CNN Framework: Identification of Right-/left-hand Motor Imagination in BCI Systems
Y. Lu,* H. Jiang, W. Liu
*corresponding author
Full text: pdf
Pre-published on: 2017 July 17
Published on: 2017 September 06
This paper described the relationship between EEG signals and MI in BCI system. EEG signals
are used to classify the direction of motioninto two kinds: left and right. We extracted features
from original EEG data using STFT and put them into CNN. The result showed that the
framework of STFT-CNN had higher average test accuracy. Furthermore, the generations of
motor imagery were analyzed, and the result showed that better classification results will appear
in the middle stage with its classification accuracy reaching 92.8%
Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.