Construction and evaluation of an emotion-inducing video dataset towards Chinese elderly healthy controls and individuals with mild cognitive impairment

This work aimed to develop and validate an emotion-inducing video dataset for the Chinese elderly. The dataset was constructed by video collection, psychological evaluation, and elderly examination. 18 videos across six emotions (neutrality, sadness, anger, happiness, boredom, and tension) were selected for emotional induction. The effectiveness of the dataset was evaluated in 37 subjects, with two groups, 21 healthy controls (HC group) and 16 individuals with mild cognitive impairment (MCI group), who were assessed in a three-session experiment. Each session comprised one pretest and six emotion-inducing videos. The electrocardiogram (ECG) and electroencephalography (EEG) signals were synchronously recorded. After viewing each video, the subjects provided self-reports of discrete emotion labels, valence, and arousal scores using a modified Self-Assessment Manikin scale. Discrete emotion analysis, valence/arousal analysis, and ECG feature analysis were conducted by the ANOVA method. EEG feature analysis was assessed with a linear mixed-effects model. Discrete emotion analysis confirmed that happiness and sadness induced by the dataset show high agreement rates (e.g., happiness: HC 0.79, MCI 0.85 and sadness: HC 0.81, MCI 0.71), whereas boredom (HC 0.38, MCI 0.29) showed a comparatively lower consistency. Valence/arousal analysis revealed significant group differences for tension and boredom emotions. ECG feature analysis revealed significant differences in the baseline-normalized mean heart rate between HC and MCI groups in specific sessions. EEG feature analysis revealed that the MCI group exhibited higher relative band power values than did the HC group in the δ and θ bands.