RESUMO
Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2 GRL) framework to model EEG signals as graph data. Specifically, MD 2 GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.
RESUMO
Objective.Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications.Approach.To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework.Main results.Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain-computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes.Significance.As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.