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 ...
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 ...
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...
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 ...
Feature extraction applied by the common spatial pattern (CSP) is very popular in MI classification. The effectiveness of CSP is highly affected by the frequency band and time window of electroencephalogram (EEG) segments and channels selected. Objective: In this study, the...
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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