论文标题
基于脑电图的情绪识别的时间空间表示学习变压器
Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition
论文作者
论文摘要
包含歧视性情绪信息的脑电图（EEG）的时间动力学和空间相关性对于情绪识别至关重要。但是，EEG信号中的一些冗余信息会降低性能。具体而言，受试者仅在刺激持续时间的一小部分就达到了潜在的激烈情绪。此外，从许多电极之间的复杂空间相关性中提取区分特征是一个挑战。为了解决这些问题，我们提出了一个基于变压器的模型，以稳健地捕获脑电图的时间动力学和空间相关性。特别是，在所有脑电图通道之间共享权重的时间提取器均设计为从原始信号中自适应提取动态上下文信息。此外，变压器内的多头自我发注意机制可以适应性地定位重要的脑电图片段，并强调有助于性能的基本大脑区域。为了验证所提出方法的有效性，我们在两个公共数据集DEAP和Mahnobhci上进行了实验。结果表明，所提出的方法在唤醒和价分类方面取得了出色的性能。
Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition. However, some redundant information within the EEG signals would degrade the performance. Specifically,the subjects reach prospective intense emotions for only a fraction of the stimulus duration. Besides, it is a challenge to extract discriminative features from the complex spatial correlations among a number of electrodes. To deal with the problems, we propose a transformer-based model to robustly capture temporal dynamics and spatial correlations of EEG. Especially, temporal feature extractors which share the weight among all the EEG channels are designed to adaptively extract dynamic context information from raw signals. Furthermore, multi-head self-attention mechanism within the transformers could adaptively localize the vital EEG fragments and emphasize the essential brain regions which contribute to the performance. To verify the effectiveness of the proposed method, we conduct the experiments on two public datasets, DEAP and MAHNOBHCI. The results demonstrate that the proposed method achieves outstanding performance on arousal and valence classification.
