2023/03/14 by Kexin Zhu, Xulong Zhang, Zhu, Kexin +7
Computer Science · Neuroscience · Psychology · #Artificial Intelligence (cs.AI) #EEG and Brain-Computer Interfaces #Emotion and Mood Recognition #FOS: Computer and information sciences #FOS: Electrical engineering #Gaze Tracking and Assistive Technology #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2303.11421
openalex publication_date 2023/03/14 · openalex created_date 2023/03/23 · openalex updated_date 2026/07/28
Using deep learning methods to classify EEG signals can accurately identify people's emotions. However, existing studies have rarely considered the application of the information in another domain's representations to feature selection in the time-frequency domain. We propose a classification network of EEG signals based on the cross-domain feature fusion method, which makes the network more focused on the features most related to brain activities and thinking changes by using the multi-domain attention mechanism. In addition, we propose a two-step fusion method and apply these methods to the EEG emotion recognition network. Experimental results show that our proposed network, which combines multiple representations in the time-frequency domain and spatial domain, outperforms previous methods on public datasets and achieves state-of-the-art at present.