<![CDATA[Emotion dysfunction can be an indication of mental health conditions like depression, stress, anxiety, etc. Therefore, appropriate recognition of human emotion is crucial for early monitoring of mental well-being. This study aims to develop a robust multilevel feature concatenation framework that effectively integrates handcrafted and deep learning (DL) features to improve electroencephalogram (EEG)-based emotion classification. The proposed framework is evaluated on two publicly available benchmark EEG datasets: DEAP and DREAMER. Initially, handcrafted features extracted from the statistical, time, frequency, and nonlinear domains are categorized into low-level features (LLF) and mid-level features (MLF). To select the most relevant handcrafted features, statistical analysis, minimum Redundancy Maximum Relevance (mRMR) and the Pearson correlation coefficient (PCC) are employed. In parallel, a two-dimensional convolutional neural network (2D-CNN) is used to learn hierarchical deep features, which are grouped into LLF, MLF, and high-level features (HLF). The handcrafted LLF and MLF are then concatenated with the corresponding DL-based LLF, MLF, and HLF to construct a discriminative multilevel feature representation. The resulting feature vector is classified using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and XGBoost classifiers under a 10-fold and leave-one-subject-out (LOSO) cross-validation protocol. Performance is evaluated using accuracy, precision, recall, F1-score, and balanced accuracy. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods. Among the evaluated classifiers, SVM achieves the highest performance, yielding accuracies of 93.07% and 98.70% for 4-class and 2-class emotion classification, respectively, on the DEAP dataset, and 93.85% and 98.78% for 4-class and 2-class emotion classification, on the DREAMER dataset. These results demonstrate that concatenation of handcrafted with DL-based features at different abstraction level significantly enhances feature discriminability and classification robustness. The proposed framework provides an effective solution for EEG-based emotion recognition, with promising applications in affective computing, human–computer interaction, and intelligent mental health monitoring.]]>
