The brain is more sensitive to stress than other organs and can develop many diseases under excessive stress. In this study, we developed a method to improve the accuracy of emotional stress recognition using multi-channel electroencephalogram (EEG) signals. The method combines a three-dimensional (3D) convolutional neural network with an attention mechanism to build a 3D convolutional gated self-attention neural network. Initially, the EEG signal is decomposed into four frequency bands, and a 3D convolutional block is applied to each frequency band to obtain EEG spatiotemporal information. Subsequently, long-range dependencies and global information are learned by capturing prominent information from each frequency band via a gated self-attention mechanism block. Using frequency band mapping, complementary features are learned by connecting vectors from different frequency bands, which is reflected in the final attentional representation for stress recognition. Experiments conducted on three benchmark datasets for assessing the performance of emotional stress recognition indicate that the proposed method outperforms other conventional methods. The performance analysis of proposed methods confirms that EEG pattern analysis can be used for studying human brain activity and can accurately distinguish the state of stress.

To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods.

This paper proposes a method of automatically classifying videos into human emotional categories by imitating human neural processes, in which emotion-specific electroencephalography (EEG) characteristics are generated by audiovisual stimulation. In the proposed method, sophisticated emotional features are first extracted from EEG signals that are generated while a subject watches a video, using a sampleattention-based deep neural network encoder. Next, the direct mapping relationship between the extracted emotional EEG features and audiovisual features extracted from the contents of the corresponding videos are learned by a deep belief network. For practical application, the EEG features corresponding to the input video are automatically generated based on the machine’s learned ability without measuring human EEG signals and subsequently applied to a segment-attention-based deep neural network decoder for emotion classification of the video. The experimental results demonstrated that the proposed method significantly outperforms existing methods with an average accuracy of about 95% for classifying four emotional classes of the video. As for automated emotional video classification, our artificial emotional EEG features-based approach obtains competitive performance, comparable to models that directly measure EEG, and can be generalized to various audiovisual datasets.