Li Chang, Wang Bin, Zhang Silin, Liu Yu, Song Rencheng, Cheng Juan, Chen Xun
Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China; Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Hefei University of Technology, Hefei, 230009, China.
Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China.
Comput Biol Med. 2022 Apr;143:105303. doi: 10.1016/j.compbiomed.2022.105303. Epub 2022 Feb 19.
Deep learning (DL) technologies have recently shown great potential in emotion recognition based on electroencephalography (EEG). However, existing DL-based EEG emotion recognition methods are built on single-task learning, i.e., learning arousal, valence, and dominance individually, which may ignore the complementary information of different tasks. In addition, single-task learning involves a new round of training every time a new task appears, which is time consuming. To this end, we propose a novel method for EEG-based emotion recognition based on multi-task learning with capsule network (CapsNet) and attention mechanism. First, multi-task learning can learn multiple tasks simultaneously while exploiting commonalities and differences across tasks, it can also obtain more data from different tasks, which can improve generalization and robustness. Second, the innovative structure of the CapsNet enables it to effectively characterize the intrinsic relationship among various EEG channels. Finally, the attention mechanism can change the weight of different channels to extract important information. In the DEAP dataset, the average accuracy reached 97.25%, 97.41%, and 98.35% on arousal, valence, and dominance, respectively. In the DREAMER dataset, average accuracy reached 94.96%, 95.54%, and 95.52% on arousal, valence, and dominance, respectively. Experimental results demonstrate the efficiency of the proposed method for EEG emotion recognition.
深度学习（DL）技术最近在基于脑电图（EEG）的情感识别中显示出巨大潜力。然而，现有的基于DL的EEG情感识别方法是基于单任务学习构建的，即分别学习唤醒度、效价和优势度，这可能会忽略不同任务的互补信息。此外，单任务学习每当出现新任务时都需要新一轮的训练，这很耗时。为此，我们提出了一种基于胶囊网络（CapsNet）和注意力机制的多任务学习的新型EEG情感识别方法。首先，多任务学习可以在利用任务间共性和差异的同时同时学习多个任务，还可以从不同任务中获取更多数据，这可以提高泛化能力和鲁棒性。其次，CapsNet的创新结构使其能够有效地表征各种EEG通道之间的内在关系。最后，注意力机制可以改变不同通道的权重以提取重要信息。在DEAP数据集中，在唤醒度、效价和优势度上的平均准确率分别达到了97.25%、97.41%和98.35%。在DREAMER数据集中，在唤醒度、效价和优势度上的平均准确率分别达到了94.96%、95.54%和95.52%。实验结果证明了所提方法用于EEG情感识别的有效性。
