Han Zhichao, Chang Hongli, Zhou Xiaoyan, Wang Jihao, Wang Lili, Shao Yongbin
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China.
The Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Southeast University, Nanjing, China.
Front Comput Neurosci. 2022 Aug 12;16:942979. doi: 10.3389/fncom.2022.942979. eCollection 2022.
Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet.
Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions.
Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset.
Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals.
This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system.
情感脑机接口可识别或调节人类情绪，用于工作量检测和精神疾病辅助诊断。然而，现有的脑电图情感识别是在特征工程和分类中逐步进行的，导致工程复杂度高，限制了其在传统脑电图情感识别任务中的实际应用。我们提出了一种端到端神经网络，即E2ENNet。
采用基线去除和滑动窗口切片对原始脑电图信号进行预处理，卷积块提取特征，长短期记忆网络（LSTM）获得特征相关性，softmax函数对情绪进行分类。
在依赖受试者的实验方案中进行了大量实验，以评估所提出的E2ENNet的性能，在三个公共数据集上达到了当前最优的准确率，即在DEAP数据集上的二分类实验中达到96.28%，在DREAMER数据集上的二分类实验中达到98.1%，在MPED数据集上的七分类实验中达到41.73%。
实验结果表明，E2ENNet可以直接从原始脑电图信号中提取更具判别力的特征。
本研究为实现即插即用的情感脑机接口系统提供了一种方法。
