Emotional Predominance of Music in Cross-Modal Affective Processing Shapes Subjective Experience and Frontal Asymmetry - Raw Dataset

Description

This dataset contains behavioral (subjective ratings), electrophysiological (EEG), and demographic data from 33 participants in a within-subjects design examining emotional processing of music, color, and their combinations. The study tested whether emotionally congruent music–color pairings produce synergistic effects or whether one modality predominates, particularly under incongruent conditions. Participants were exposed to eight conditions: individual stimuli (M+ = pleasant music, M− = unpleasant music, C+ = pleasant color, C− = unpleasant color) and combined stimuli (M+C+ and M−C− = congruent; M+C− and M−C+ = incongruent). Subjective ratings include Valence (V), Arousal (I), and Pleasure (P), as well as a composite index (VIP). For combined stimuli, Congruence (C) and Predominance (Pr) were also collected, with values ranging from −100 (music dominance) to +100 (color dominance). The data show systematic variability in emotional predominance, with a general tendency toward music-driven evaluations. EEG data include spectral power estimates for frontal (F3, F4) and parietal (P3, P4) electrodes in the theta (TH) and alpha (AL) bands. Interhemispheric asymmetry (IHA) was computed as right minus left activity (e.g., F4 − F3; P4 − P3), where positive values indicate relatively greater right-hemisphere activation. Variable names combine condition, electrode or asymmetry index, and frequency band (e.g., M+C−_F3_TH; M−C+_IHA_F_TH). Values correspond to averages across artifact-free epochs within each condition and participant. These measures allow examination of how emotional predominance relates to hemispheric asymmetry under cross-modal conflict. Demographic variables (in Spanish) include Sexo, Nacionalidad, Escolaridad, Ocupación, Escucha musical, Preferencia musical, Educación musical, BDI, and Duración. Fully commented Python scripts used in the analyses are also provided (pandas, NumPy, SciPy, statsmodels, Pingouin, scikit-posthocs, Matplotlib, Seaborn), enabling reproducibility and further analysis of multimodal emotional processing.

Institutions

• Universidad Autónoma del Estado de Morelos

  Morelos, Cuernavaca

Categories

Licence