Figure 2.

Pipeline of hybrid 1D convolutional neural network (1D-CNN) prediction model. We first extracted the blood-oxygen-level-dependent (BOLD) signal from a specified target voxel (e.g., Occiptial_Mid_L: left middle occipital gyrus) for each subject under each condition (i.e., SI or VG). We then applied continuous wavelet transforms (CWTs) to the time-series to generate time-frequency decomposition across continuous scales. The transformed time-series of each frequency band were then imported to the 1D-CNN model to predict the continuous state.