Abstract
It is the key of brain-computer interface technology to extract electroencephalogram (EEG) data features effectively and classify them accurately. In view of the characteristics of non-stationarity and obvious time-frequency characteristics of motor imagery EEG signals, this paper proposes a method for recognition of motor imagery EEG signals based on S-transform time-frequency image combined with convolutional neural network (CNN) and extreme learning machine (ELM). In the BCI competition dataset, firstly, the S-transform time-frequency image of C3 and C4 electrode signals is obtained, and then the characteristic frequency bands are extracted from the time-frequency image for combination. Finally, the combined image is used as the input of neural network to realize the recognition of left-right hand motor imagery EEG signals. Experimental results show that this method is superior to the ordinary convolutional neural network.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
