Analysis of Dimension Reduction by PCA and AdaBoost on Spelling Paradigm EEG Data

Date

2013-12-18

Author

YILDIRIM, Asil
Halıcı, Uğur

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Spelling Paradigm is a BCI application which aims to construct words by finding letters using P300 signals recorded via channel electrodes attached to the diverse points of the scalp. In this study effects of dimension reduction using Principal Component Analysis (PCA) and AdaBoost methods on time domain characteristics of P300 evoked potentials in Spelling Paradigm are analyzed. Support Vector Machine (SVM) is used for classification.

Subject Keywords

Brain computer interfaces, Spelling paradigm, Principal component analysis, Adaboost, Support vector machines

URI

https://hdl.handle.net/11511/41930

DOI

https://doi.org/10.1109/bmei.2013.6746932

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

A. YILDIRIM and U. Halıcı, “Analysis of Dimension Reduction by PCA and AdaBoost on Spelling Paradigm EEG Data,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41930.