Enhanced emotion detection in EEG using CNN1D and RNN hybrids with adaptive optimization

Emotion identification using electroencephalogram (EEG) data is now recognised as a critical strategy for developing intelligent human-computer interaction (HCI) and brain-computer interface (BCI) technologies. This paper explores the effectiveness of three hybrid deep learning (DL) frameworks—CNN1D-LSTM, CNN1D-GRU, and GRU-LSTM—for categorizing emotional states utilizing EEG signals from an online available dataset. Every algorithm was trained and assessed using three optimization strategies: Adam, RMSprop, and Stochastic Gradient Descent (SGD). Experimental findings show that when optimized with the Adam method, the CNN-GRU outperformed with an F1 score of 99.5% and a 5-fold cross-validation average F1 Score of 97.5% (95% CI 95.8–99.1%). Furthermore, statistical analyses, including t-tests, p -values, Cohen’s d, Glass’s Δ, and ANOVA, confirmed the model’s significance and stability. The results demonstrate the durability and reliability of hybrid DL models in capturing the complex temporal and spatial correlations in EEG data. The findings have important implications for developing real-time affective computing platforms and improving the usability of EEG-based emotion recognition in real-world HCI and BCI systems.