Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment

With the rapid progress in data-driven approaches, artificial intelligence, and big data analytics technologies, utilizing electroencephalogram (EEG) signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases. While existing emotion analysis methods focus on the utilization of effective deep models for data-driven and big data analytics technology, they often struggle to extract long-range dependencies and accurately model local relationships within multi-channel EEG signals. In addition, the subjective scores of the subjects may not match the predefined emotional labels. To overcome these limitations, this paper proposes a new data-driven dynamic graph-embedded Transformer network (DGETN) that has emerged in different tasks of graph data mining for emotion analysis of EEG signals in the scene of IoMT. Firstly, we extract the frequency features differential entropy (DE) and use the linear dynamic system (LDS) method to alleviate the redundancy and noise information. Secondly, to effectively explore the long-range information and local modeling ability, a novel feature extraction module is designed by embedding the dynamic graph convolution operations in the Transformer encoder for mining the discriminant features of data. Moreover, the graph convolution operations can effectively exploit the spatial information between different channels. At last, we introduce the minimum category confusion (MCC) loss to alleviate the fuzziness of classification. We take two commonly used EEG sentiment analysis datasets as a study. The DGETN has achieved state-of-the-art accuracies of 99.38% on the SEED dataset, and accuracies of 99.24 % and 98.85% for valence and arousal prediction on the DEAP dataset, respectively.