Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal.
Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is...
Main Authors: | Khatereh Darvish Ghanbar, Tohid Yousefi Rezaii, Ali Farzamnia, Ismail Saad |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0248511 |
Similar Items
-
Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework
by: Mehdi Abdollahpour, et al.
Published: (2020-01-01) -
An Adaptive CSP and Clustering Classification for Online Motor Imagery EEG
by: Qin Jiang, et al.
Published: (2020-01-01) -
Classification of Motor Imagery EEG Signals using Improved CSP based on Riemannian Geometry
by: Ji-En Ke, et al.
Published: (2019) -
Improving Motor Imagery EEG Signals Classification Accuracy with CSP by Available Machine Learning Approach
by: Umme Farhana, et al.
Published: (2021-06-01) -
Automatic Identification of Epileptic Seizures From EEG Signals Using Sparse Representation-Based Classification
by: Sobhan Sheykhivand, et al.
Published: (2020-01-01)