This study investigates the sensitivity of EMD-dependent feature extraction methods to SNR in EEG signals for BCI applications.
SNR sensitivity of EMD methods cautions BCI deployment in noisy EEG; leaves optimal feature extraction open for validation.
One of the most complicated biological signals is EEG which its processing has vast applications in Brain-Computer Interface (BCI) Speller is very important. For P300 detection in EEG, miscellaneous Single-Trial and Multi-Trial methods have been introduced, with their own advantages and disadvantages. Our purpose in this paper was to investigate Empirical Mode Decomposition-Dependent (EMD-Dependent) methods of feature extraction sensitivity to Signal to Noise Ratio (SNR) of original signal.
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Arasteh et al. (2010) studied this question.
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