EEG Emotion Recognition Based on GADF and AMB-CNN Model

Abstract

Deep learning has achieved better results in natural language processing, computer vision, and other fields. Nowadays, more deep learning algorithms have also been applied in brain-based emotion recognition. In the studies on brain-based emotion recognition, deep learning models typically use one-dimensional time series as the input and cannot fully leverage the advantages of the models in image classification or recognition. To address this issue, based on the publicly available SEED and DEAP datasets, the Gramian angular difference field (GADF) method was proposed to construct two-dimensional image representation datasets: SEED-GADF and DEAP-GADF datasets, in the paper. Additionally, a convolutional attention mechanism model (AMB-CNN) was introduced and its classification performance was validated on SEED-GADF and DEAP-GADF datasets. AMB-CNN achieved an average accuracy of 90.8%, a recall rate of 90%, and AUC of 96.86% on SEED-GADF. On DEAP-GADF, the average accuracy, recall rate, and AUC respectively reached 96.06%, 96.06%, and 98.58% in the valence dimension and 96.11%, 96.11%, and 98.73% in the arousal dimension. Finally, the comparison results with various algorithms and ablation experiments proved the superiority of the proposed model.