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- W3157482046 abstract "This examination portrays the utilization of back spread Neural Network as an order and Discrete Wavelet Transformation for highlight extraction by taking vitality esteems on each sub-band of the Electro Encephalo Graph (EEG) signal wave. The reason for this examination was to distinguish the EEG signal utilized in the cursor development. The point of this investigation was to give new correspondence and control alternatives for individuals with serious engine inability. The information utilized are EEG information got from BCI rivalry 2003 (BCI Competition 2003). This information contains class 0 information (for upward cursor development) and class 1 (for descending cursor development). Dynamic is done in two phases. In the principal organize, the vitality esteems in each discrete wavelet sub-band are utilized to extricate highlights of the EEG signal information. This element is as the contribution on back engendering Neural Network. The subsequent stage is distinguishing process into two classes (class 0 and class 1) of EEG signal information records. There are 260 preparing information records of EEG signs and 293 of the EEG signal information document testing, so the all-out is 553 information records of EEG signals." @default.
- W3157482046 created "2021-05-10" @default.
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- W3157482046 date "2019-12-01" @default.
- W3157482046 modified "2023-09-23" @default.
- W3157482046 title "COLLECTIVE ELECTRO ENCEPHALO GRAPH SIGNALS BY MEANS OF WAVELET TRANSFORMS" @default.
- W3157482046 hasPublicationYear "2019" @default.
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