| 基于脑电信号与集成分类器的心理负荷评估 |
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| 引用本文： | 顾 浩,尹 钟.基于脑电信号与集成分类器的心理负荷评估[J].教育技术导刊,2019,18(11):1-4. |
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| 作者姓名： | 顾 浩 尹 钟 |
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| 作者单位： | 上海理工大学 光电信息与计算机工程学院，上海 200093 |
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| 基金项目： | 国家自然科学基金青年项目（61703277）；上海青年科技英才扬帆计划项目（17YF1427000） |
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| 摘 要： | 为了精确评估个体心理负荷状态，需要获取目标脑电信号数据，脑电信号是评估脑力负荷变化的重要指标。机器学习和神经网络越来越多地用于脑力负荷分类。利用脑电信号特征可在时域和频域中提取突出信息。因此提出一个结合支持向量机（SVM）与超限学习机（ELM）的混合型脑力负荷分类框架。其中支持向量机作为成员分类器，可在高维EEG特征中查找隐藏信息|超限学习机用于融合成员分类器的输出。将ELM-SVM模型与经典脑力负荷分类器进行比较，得出该模型训练精度准确率为1，且测试精度提升0.1个百分点。
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| 关 键 词： | 脑力负荷 脑电信号 超限学习机 支持向量机 |
| 收稿时间： | 2019-09-06 |
Mental Workload Assessment Based on EEG and A Hybrid Ensemble Classifier |
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| GU Hao,YIN Zhong.Mental Workload Assessment Based on EEG and A Hybrid Ensemble Classifier[J].Introduction of Educational Technology,2019,18(11):1-4. |
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| Authors: | GU Hao YIN Zhong |
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| Institution: | School of Optical and Computer Engineering， University of Shanghai for Science and Technology， Shanghai 200093，China |
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| Abstract: | In order to assess the state of mental workload， it is necessary to obtain the EEG signal data of the target. The EEG signals can be used as an important indicator to assess the variation of the mental workload. Machine learning and neural networks are increasingly used for mental workload classification. By extracting the features of the EEG signals， the salient information can be extracted in the temporal and frequency domains. In this work， we applied a hybrid mental workload classification framework that combines extreme learning machine （ELM） and the support vector machine （SVM）. The former is used as the member classifier to find hidden information in high-dimensional EEG features. The latter is used to fuse the outputs of the member classifier. Finally， we compare the proposed ELM-SVM model with the classic mental load classifier， the results show that the training accuracy can be increased to 1， and the test accuracy is also improved by 0.1%. |
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| Keywords: | mental workload EEG extreme learning machine support vector machine |
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