Snr Criterion Maximization for the Extraction of Erp from Eeg Data
نویسندگان
چکیده
In this article, signal-to-noise ratio (SNR) criterion is investigated with the object of determining the level of its ability to distinguish between Event Related Potentials (ERPs) and noise signals in ElectroEncephaloGraphy (EEG) data. For this purpose a new algorithm (SNRMAX) based on SNR maximization and intended for extracting a linear subspace related to ERPs from EEG data is developed. The algorithm is compared to the previously developed one (ERPSUB) using real EEG data set. This comparison gives an important interpretation of the results of ERPSUB in terms of SNR and practically proves that ERPSUB performs most optimally in the sense of SNR.
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