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An efficient P300 detection algorithm based on Kernel Principal Component Analysis-Support Vector Machine
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DOI：10.1016/j.compeleceng.2021.107608.png)
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Human-machine interaction using brain signals has been made possible by the advent of a technology popularly known as a brain-computer interface (BCI). P300 is the most studied event related potentials (ERP) and is used in many BCI systems. The existing multi-trial P300 detection methods suffer drawbacks of being time-consuming and computationally complex, whereas, the existing single-trial methods are apt at achieving only moderate accuracy levels. In this paper, a novel approach to achieve a high level of accuracy for a single trial P300 signal detection amidst noise and artifacts. In this method, features were obtained from wavelet coefficients; subsequently, feature dimensions were reduced, thereby enhancing the speed of classification along with a manifold improvement in the accuracy of P300 signal classification. An accuracy of 98.53% was achieved for Subject S1 and 99.25% for Subject S2 using the proposed method. A high level of accuracy was obtained, compared to many existing techniques. Moreover, the speed of classification improved with the use of reduced feature dimensions.
Keyword:
Single-trial P300 detection
Brain-Computer Interface Technology (BCI)
Kernel Principal Component Analysis (KPCA)
Discrete Wavelet Transform (DWT)
Support Vector Machine (SVM)
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