The use of multi-dimensional information technology to process auditory synesthesia in musical emotions has gradually become a hot topic of interest among many scholars. This paper proposes a novel method for decoding electroencephalography (EEG) signals by combining spectral summation analysis (SSA) with entropy measures. EEG time series are decomposed and reconstructed using SSA to obtain SSA components of various orders. Four entropy measure feature data—approximate entropy, sample entropy, fuzzy entropy, and multiscale entropy—are extracted from the SSA components. These entropy measure feature data are then used to construct a feature vector describing the relevant EEG data decoding recognition, and combined with a pattern classifier to achieve EEG decoding. Additionally, a TCM-CSP emotional EEG analysis method based on cognitive topological constraints is proposed. Considering the temporal changes in emotions, an emotional brain region analysis method is proposed, and combined with the spatial characteristics of EEG signals, a CSP computation method that preserves topological constraints is proposed. Finally, experiments show that in emotional tendency analysis, the distortion group and the reverb group exhibit negative correlations. The distortion group tends toward restlessness and anxiety, while the reverb group tends toward calmness and relaxation, indicating that the tonal quality of sound after different effects processing significantly influences emotional perception.
