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• W3125862984 abstract "The rapid growth in brain–computer interfaces (BCIs) technology with its new applications has helped many patients to help in rehabilitation from various diseases. This paper analyzes and compares two feature extraction algorithms (i) common spatial patterns (CSP) and (ii) Riemannian geometry-based algorithm for motor imagery-based EEG classification. Also, this study discusses some of the challenges and limitations of these algorithms and performance evaluation of these two techniques. Publicly available BCI Competition IV dataset IIa has been used to show the empirical result analysis. This shows the Riemannian geometry-based algorithm outperforms the CSP feature extraction algorithm. Riemannian features + SVM classifier gives the accuracy of 74.77% whereas CSP feature + SVM gives the accuracy of 73.82%. Also, we found Riemannian features are trained 3 times faster than CSP features and evaluation is done 4 times faster on Riemannian features." @default.
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• W3125862984 date "2021-01-01" @default.
• W3125862984 modified "2023-10-18" @default.
• W3125862984 title "Common Spatial Pattern Versus Riemannian Features for Motor Imagery EEG Classification for Brain–Computer Interface" @default.
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• W3125862984 doi "https://doi.org/10.1007/978-981-15-9689-6_27" @default.
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