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- W2955367213 abstract "Convolutional neural network (CNN) has a wide range of applications in the field of computer vision, but it is rarely used to process raw electroencephalograph (EEG) signals. On the one hand, raw EEG signal is the timing signal of multiple electrodes, so it’s impossible to extract local features like a picture using convolutional filters. On the other hand, EEG data collection is rather cumbersome, so the amount of EEG data is much smaller than some large datasets such as ImageNet, which leads to deeper network more prone to overfitting on EEG datasets. In this paper, we propose a method for processing resting state EEG signals, which uses Fourier transform to convert time-series EEG signals into frequency-domain signals, and construct a CNN model according to the characteristics of EEG signal. We applied this method to personal identification and classification of gender and age. For personal identification, we achieved an accuracy of 95.5% on our dataset, and the accuracy of gender and age classification was 62.7% and 51.2% respectively." @default.
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- W2955367213 date "2019-05-01" @default.
- W2955367213 modified "2023-09-26" @default.
- W2955367213 title "Convolutional Neural Network for Resting State EEG Signal Analysis" @default.
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- W2955367213 doi "https://doi.org/10.1109/icnsc.2019.8743338" @default.
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