[Retracted] Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
Table 2
M-3DCNN algorithm pseudocode.
M-3DCNN algorithm
1. begin
2. Input: the emotion-related EEG topographic map is divided into training set and test set , and the spatial neighborhood size is . The samples of training set and test set are normalized
3. Output: overall classification accuracy OA of confusion matrix, average classification accuracy AA, image classification results
4. for each pixel
5. Clipping the spatial neighborhood size of
6. Mixup
7. Initialize the weight of the Mixup method
8. for each training sample in the mini_batch
9. for , do
10.
11.
12. end for
13. Train and test the optimal M-3dCNN network
14. Initialization learning rate ξ, network weight , network offset b, and set the number of iterations of training epoch
15. Network training
16. The image data to be classified is input into the trained M-3DCNN network to predict the category of the target
17. Calculate OA, AA
18. The classification results of emotion-related EEG topographic map were obtained
