We applied joint CSP to a simultaneous EEG-fMRI dataset collected from 21 subjects under two different resting-state conditions (eyes-closed and eyes-open). Results show a distinct dynamic pattern shared by EEG alpha power and fMRI signal during eyes-open resting-state....
The Common Spatial Pattern (CSP) and other variants have shown considerable progress in MI classification (Ang et al., 2008). For instance, Alizadeh et al. (2023) proposed a multi-class EEG signal classification method based on empirical modal decomposition (EMD) and multi-class common Methods...
IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013. Computational Disaster Management. Pascal Van Hentenryck 电商所评分:2 原文链接 谷歌学术 必应学术 百度学术 Soft Robotics: The Next Generation of Intelligent Machines. ...
Approximation joint diagonalization (AJD) based multiclass common spatial pattern (CSP) algorithm is utilized for feature extraction and k-nearest neighbor (KNN) is used for classification. The algorithm was tested and compared with three typical methods on a four-class motor imagery data set The ...
Common spatial pattern (CSP) algorithm is most frequently applied for feature engineering in motor imagery (MI) based BCI system. How to select the most suitable spatial channels, temporal & frequency parameters for different people before CSP is still a challenging issue which greatly affects the ...
We present a novel method based on joint tensor diagonalization for selecting or weighting electroencephalogram (EEG) data to estimate the covariance matrices to accurately find common spatial pattern (CSP). CSP and its variants need a pair of covariance matrices of two different tasks, which are ...
The effectiveness of common spatial pattern (CSP) feature, which is commonly used in Electroencephalogram (EEG) data analysis and EEG-based brain computer interfaces (BCIs), can be explained by Rayleigh coefficient maximization. Two other features are also defined using the Rayleigh coefficient. These...
In this work, we employ the Pearson correlation-based channel selection method to establish high-quality spatial distribution. Moreover, a novel multiband based joint sparse representation (MJSR) is proposed to fuse CSP features of multiband and obtain joint coefficient features. The SVM is then ...
It combines the multihead self-attention into the original CSPDarkNet to achieve effective cross-scale feature fusion. Zhang et al. [36] proposed an attractive one-stage network for UOD, which combines the MobileNetv2 and depth-wise separable convolution to effectively reduce the computational load...
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