Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations
This work addresses emotion recognition from EEG signals, which is an incremental improvement for applications in affective computing and brain-computer interfaces.
The paper tackled EEG-based emotion recognition by fusing time-frequency and spatial representations using a cross-domain feature fusion method with multi-domain attention, achieving state-of-the-art performance on public datasets.
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.