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- W4225537608 abstract "Visual P300 mind speller is a brain-computer interface allowing an individual to type through his mind. To this aim, the subject sits in front of a screen full of letters, and when his desired one flashes, there will be a P300 response (a positive deflection nearly 300ms after stimulus) in his brain signals. Due to the very low signal-to-noise (SNR) of the P300 in the background activities of the brain, detection of this component is challenging. Principal ERP reduction (pERP-RED) is a newly developed method that effectively extracts the underlying templates of event-related potentials (ERPs) by employing a three-step spatial filtering procedure. In this research, we investigate the performance of pERP-RED in conjunction with linear discriminant analysis (LDA) to classify P300 data. The proposed method is examined on a real P300 dataset and compared to the state-of-the-art LDA and support vector machines. The results demonstrate that the proposed method achieves higher classification accuracy in low SNRs and low numbers of training data." @default.
- W4225537608 created "2022-05-05" @default.
- W4225537608 creator A5079098947 @default.
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- W4225537608 date "2021-12-29" @default.
- W4225537608 modified "2023-10-17" @default.
- W4225537608 title "A new approach based on principal ERPs and LDA to improve P300 mind spellers" @default.
- W4225537608 doi "https://doi.org/10.1109/icspis54653.2021.9729346" @default.
- W4225537608 hasPublicationYear "2021" @default.
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