Cheng Zhuoling, Bu Xuekui, Wang Qingnan, Yang Tao, Tu Jihui
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, 434100, Hubei, China.
School of Physics, Electronics and Intelligent Manufacturing, Huaihua University, Hunan, 418000, China.
Sci Rep. 2024 Dec 28;14(1):31319. doi: 10.1038/s41598-024-82705-z.
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer. First, the Multi-Scale Dynamic CNN is used to extract complex spatial and spectral features from raw EEG signals, which not only avoids information loss but also reduces computational costs associated with the time-frequency conversion of signals. Then, the Gated Transformer Encoder is utilized to capture global dependencies of EEG signals. This encoder focuses on specific regions of the input sequence while reducing computational resources through parallel processing with the improved multi-head self-attention mechanisms. Third, the Temporal Convolution Network is used to extract temporal features from the EEG signals. Finally, the extracted abstract features are fed into a classification module for emotion recognition. The proposed method was evaluated on three publicly available datasets: DEAP, SEED, and SEED_IV. Experimental results demonstrate the high accuracy and efficiency of the proposed method for emotion recognition. This approach proves to be robust and suitable for various practical applications. By addressing challenges posed by existing methods, the proposed method provides a valuable and effective solution for the field of Brain-Computer Interface (BCI).
情绪在人类思维、认知过程和决策中起着至关重要的作用。脑电图（EEG）因其高时间分辨率、实时监测能力、便携性和成本效益，已成为情绪识别中广泛使用的工具。在本文中，我们提出了一种基于多尺度动态一维卷积神经网络（Multi-Scale Dynamic 1D CNN）和门控变换器（Gated Transformer）的从脑电信号中进行端到端情绪识别的新方法，称为MSDCGTNet。首先，多尺度动态卷积神经网络用于从原始脑电信号中提取复杂的空间和频谱特征，这不仅避免了信息丢失，还降低了与信号时频转换相关的计算成本。然后，使用门控变换器编码器来捕捉脑电信号的全局依赖性。该编码器专注于输入序列的特定区域，同时通过改进的多头自注意力机制进行并行处理来减少计算资源。第三，时间卷积网络用于从脑电信号中提取时间特征。最后，将提取的抽象特征输入到分类模块进行情绪识别。该方法在三个公开可用的数据集上进行了评估：DEAP、SEED和SEED_IV。实验结果证明了该方法在情绪识别方面的高精度和高效率。这种方法被证明是稳健的，适用于各种实际应用。通过应对现有方法带来的挑战，该方法为脑机接口（BCI）领域提供了一种有价值且有效的解决方案。
