School of Physical Education, Jiamusi University, Jiamusi 154000, China.
School of Physical Education, Nanchang Normal University, Nanchang 330032, China.
Comput Intell Neurosci. 2022 Apr 15;2022:6752067. doi: 10.1155/2022/6752067. eCollection 2022.
Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.
情绪是人类在与外部环境相互作用过程中对外界刺激产生的一种重要表现形式，它无时无刻不在影响着我们生活的方方面面。准确识别人类的情绪状态并进一步应用于人工智能中，可以更好地改善和辅助人类的生活。因此，情绪识别的研究近年来引起了人工智能领域许多学者的关注。脑电信号的转换变得至关重要，需要一种脑电信号处理方法来提取有效信号，以实现人机交互。然而，EEG 信号的非平稳非线性特征给特征信号提取带来了巨大的挑战。目前，虽然有许多特征提取方法，但没有一种方法能够反映信号的全局特征。以下解决方案用于解决上述问题：（1）本文提出了一种基于 ICA 和样本熵算法的 EEG 信号特征提取框架，该框架尚未应用于 EEG；（2）使用仿真信号验证了该方法的可行性，并在两个真实数据集上进行了实验，以展示新算法在 EEG 信号特征提取中的优势。
