Emotion recognition is the process of identifying human emotions. It allows for the understanding and interpretation of emotional states. Furthermore, emotion recognition has the potential to improve human-machine interaction, making technology more intuitive and responsive to human emotions. In the ICBHI 2024 Scientific Challenge, it was challenged to recognize emotions by predicting both the valence class and valence level. The overall performance of emotion recognition is determined by a weighted sum of the errors in predicting the valence class and valence level. In this study, the wavelet scattering transform was examined and applied to BOLD data, converted from fMRI data, for emotion recognition. The wavelet scattering coefficients obtained from all optimized scattering paths of the wavelet scattering network are used as quantitative features of BOLD data, and a feedforward, fully connected neural network was used as a classifier. The effects of different formations of wavelet scattering feature vectors and structures of neural network models on emotion recognition are investigated. Computational results demonstrate that both the valence class and valence level can be remarkably predicted using the wavelet scattering features of BOLD data. The best performance of emotion recognition achieved in the ICBHI 2024 Scientific Challenge is the score of 0.3624.