EEG-Based Recognition of Self-Imagined Emotions Using Multi-Domain Features and Subject-Specific Calibration

Self-imagined emotional imagery, in which individuals internally generate emotional states without external induction, plays a key role in shaping affective brain–computer interface (BCI) systems. Robust generalization across individuals is challenging due to significant variability in EEG patterns. This study evaluates a multi-domain electroencephalography (EEG) decoding framework for self-imagined emotion recognition, integrating time–frequency features, functional connectivity, effective connectivity, and subject-specific calibration. EEG data were collected from 38 participants and categorized into four emotional conditions using the valence–arousal model. Time–frequency features were extracted using the wavelet transform; functional connectivity was assessed using the phase locking value (PLV) and Pearson’s correlation; and effective connectivity was estimated using Granger causality. For comparison, a two-dimensional convolutional neural network (CNN) based on the continuous wavelet transform was implemented. Under pooled sample-wise cross-validation, PLV-based features combined with a support vector machine (SVM) achieved 97.0% accuracy, while the CNN reached 94.75%. Under leave-one-subject-out evaluation, accuracy declined to approximately 25–30%, indicating limited generalization to unseen participants. With subject-specific calibration using 75% of the target participant’s data, the SVM achieved 95.0% accuracy. The results support the recognition of four emotional conditions under the evaluated protocol when data from the target participant are available. However, cross-subject generalizability remains a significant limitation. As the study was conducted under controlled experimental conditions, further research involving independent populations and experimental settings is necessary to evaluate the framework’s robustness and applicability.