Abstract
Electroencephalography (EEG)-basedpersonal identification has gained significant attention, but fluctuations in emotional states often affect model accuracy. Previous studies suggest that multisensory stimuli, such as video and olfactory cues, can enhance emotional responses and improve EEG-based identification accuracy. This study proposes a novel deep learning-based model, CNN-BiLSTM-Residual Network (CBR-Net), for EEG-based identification and establishes a multisensory emotional EEG dataset with both video-only and olfactory-enhanced video stimulation. The model includes a convolutional neural network (CNN) for spatial feature extraction, Bi-LSTM for temporal modeling, residual connections, and a fully connected classification module. Experimental results show that olfactory-enhanced video stimulation significantly improves the emotional intensity of EEG signals, leading to better recognition accuracy. The CBR-Net model outperforms video-only stimulation, achieving the highest accuracy for negative emotions (96.59%), followed by neutral (94.25%) and positive emotions (95.42%). Ablation studies reveal that the Bi-LSTM module is crucial for neutral emotions, while CNN is more effective for positive emotions. Compared to traditional machine learning and existing deep learning models, CBR-Net demonstrates superior performance across all emotional states. In conclusion, CBR-Net enhances identity recognition accuracy and validates the advantages of multisensory stimuli in EEG signals.