Add like
Add dislike
Add to saved papers

Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.

OBJECTIVE: Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.

APPROACH: To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy (SSCE) feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, Approximate Entropy (ApEn), Fuzzy Entropy (FE) and Permutation Entropy (PE) were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using Support Vector Machine (SVM).

MAIN RESULT: The proposed method was validated on two MI public datasets (BCI competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.

SIGNIFICANCE: The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.&#xD.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app