Faculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of China.
JD Health International Inc., Beijing, People's Republic of China.
J Neural Eng. 2024 May 15;21(3). doi: 10.1088/1741-2552/ad4743.
. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting.. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets.. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios.. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.
脑电图（EEG）分析一直是神经工程中的重要工具，而人类情绪的识别和分类是神经工程中的重要任务之一。从头皮上放置的电极获得的 EEG 数据是脑活动分析和情绪识别的宝贵信息资源。特征提取方法已经取得了有希望的结果，但最近的趋势已经转向基于深度学习的端到端方法。然而，这些方法往往忽略了通道表示，并且它们的复杂结构对模型拟合提出了一定的挑战。为了解决这些挑战，本文提出了一种名为 FetchEEG 的混合方法，该方法结合了特征提取和时间-通道联合注意。利用传统特征提取和深度学习的优势，FetchEEG 采用多头自注意机制同时提取不同时间点和通道之间的表示。然后，将联合表示进行串联，并使用全连接层进行分类，以进行情绪识别。通过在自行开发的数据集和两个公共数据集上进行对比实验验证了 FetchEEG 的性能。在依赖于主体和独立于主体的实验中，FetchEEG 在所有数据集上的表现都优于最先进的方法，具有更好的性能和更强的泛化能力。此外，还分析了特征提取模块中不同滑动窗口大小和重叠率对 FetchEEG 性能的影响。研究了三频带和五频带场景下情绪识别的敏感性。FetchEEG 是一种基于 EEG 的新型混合方法，用于情绪分类，它将 EEG 特征提取与 Transformer 神经网络相结合。它在自行开发的数据集和多个公共数据集上都取得了最先进的性能，与端到端方法相比，训练效率显著提高，证明了其有效性和可行性。
