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- W3202364459 abstract "Abstract Artificial intelligence has been widely used in the field of biological signal recognition. However, most researches use deep learning to classify emotions, which has limitations in its application in the medical field. To this end, this paper proposes a one-dimensional convolutional neural network (1D-CNN) model for regression tasks. After we standardize, transform and slice the data, we divide the training set, validation set, and test set at a ratio of 8:1:1, and feed the data into the neural network for training to achieve emotion prediction. Experiments on the DEAP dataset show that the model we built has good performance for emotion prediction, which provides new insights for the medical field. The source codes are available at https://github.com/gjm-web/1D-CNN." @default.
- W3202364459 created "2021-10-11" @default.
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- W3202364459 date "2021-09-01" @default.
- W3202364459 modified "2023-09-26" @default.
- W3202364459 title "Emotion Prediction of EEG Signals based on 1D Convolutional Neural Network" @default.
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- W3202364459 doi "https://doi.org/10.1088/1742-6596/2024/1/012044" @default.
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