Emotion Recognition From Multi-Channel EEG via Deep Forest
Emotion Recognition From Multi-Channel EEG via Deep Forest
复制标题
通过深森林的多通道脑电图进行情绪识别
DOI：
10.1109/jbhi.2020.2995767
复制
发表时间：
2021-02-01
影响因子：
7.7
通讯作者：
Chen, Xun
中科院分区：
文献类型：
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作者：
Cheng, Juan;Chen, Meiyao;Chen, Xun
最近，深度神经网络（DNN）已被应用于基于脑电图（EEG）的情感识别任务，并取得了比传统算法更好的性能。然而，DNN仍然存在超参数太多和训练数据太多的缺点。为了克服这些缺点，在这篇文章中，我们提出了一种基于多通道EEG的情感识别方法，使用深度森林。首先，我们考虑基线信号的影响，对去除伪迹的原始EEG信号进行基线去除预处理。其次，我们构造2$D$帧序列，考虑到空间位置关系的通道。最后，将2$D$帧序列输入到由深度森林构造的分类模型中，该模型可以挖掘脑电信号的空间和时间信息，从而对脑电情感进行分类。该方法无需传统方法中的特征提取，分类模型对超参数设置不敏感，大大降低了情感识别的复杂度。为了验证该模型的可行性，在两个公开的DEAP和DREAMER数据库上进行了实验。在DEAP数据库上，效价和唤醒的平均准确率分别达到97.69%和97.53%;在DREAMER数据库上，效价、唤醒和优势的平均准确率分别达到89.03%、90.41%和89.89%。这些结果表明，所提出的方法具有更高的精度比国家的最先进的方法。
Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks based on electroencephalography (EEG), and have achieved better performance than traditional algorithms. However, DNNs still have the disadvantages of too many hyperparameters and lots of training data. To overcome these shortcomings, in this article, we propose a method for multi-channel EEG-based emotion recognition using deep forest. First, we consider the effect of baseline signal to preprocess the raw artifact-eliminated EEG signal with baseline removal. Secondly, we construct 2$D$ frame sequences by taking the spatial position relationship across channels into account. Finally, 2$D$ frame sequences are input into the classification model constructed by deep forest that can mine the spatial and temporal information of EEG signals to classify EEG emotions. The proposed method can eliminate the need for feature extraction in traditional methods and the classification model is insensitive to hyperparameter settings, which greatly reduce the complexity of emotion recognition. To verify the feasibility of the proposed model, experiments were conducted on two public DEAP and DREAMER databases. On the DEAP database, the average accuracies reach to 97.69% and 97.53% for valence and arousal, respectively; on the DREAMER database, the average accuracies reach to 89.03%, 90.41%, and 89.89% for valence, arousal and dominance, respectively. These results show that the proposed method exhibits higher accuracy than the state-of-art methods.