Accurate recognition of human emotions is essential in many fields such as education, healthcare, and entertainment, and is particularly valuable for improving the adaptability of brain-computer interface (BCI) systems in human-computer interactions. Therefore, in this study, we propose a hybrid deep learning approach that combines a one-dimensional convolutional neural network (1D-CNN) with a Transformer encoder, referred to as the 1D-CNN-Transformer, for emotion classification based on electroencephalogram (EEG) signals using two subsets of selected channels. RF-RFE is employed to select the most informative EEG channels, followed by the extraction of time-domain and frequency-domain features across four frequency bands decomposed by the discrete wavelet transform (DWT). These features are evaluated using three models including 1D-CNN, Multilayer perceptron, and the proposed 1D-CNN-Transformer, with subject-wise 5-fold cross-validation on the validation dataset. Among the models, the hybrid 1D-CNN-transformer model achieves the best results, with 70.0% accuracy, 72.9% precision, 82.7% recall, and a 76.7% F1-score for valence, and 67.5% accuracy, 68.89% precision, 82.71% recall, and a 73.9% F1-score for arousal.