基于线性核主成分分析和XGBoost的脑电情感识别

董寅冬; 任福继; 李春彬

doi:10.12086/oee.2021.200013

基于线性核主成分分析和XGBoost的脑电情感识别

doi: 10.12086/oee.2021.200013

董寅冬^{1, 2,},
任福继^{1, 2, 3},
李春彬^{1, 2}

基金项目:

国家自然科学基金-深圳联合基金重点项目 U1613217

详细信息

作者简介:
董寅冬(1987-)，男，博士研究生，主要从事情感计算、人工智能以及类脑计算的研究。E-mail：dongyindong66@163.com

中图分类号: TP18; TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2020-01-07
- 修回日期: 2020-06-11

EEG emotion recognition based on linear kernel PCA and XGBoost

Dong Yindong^{1, 2
,},
Ren Fuji^{1, 2, 3},
Li Chunbin^{1, 2}

Funds:

National Natural Science Foundation of China-Shenzhen Joint Foundation (Key Project) U1613217

摘要

摘要: 本文通过引入线性核的主成分分析和极端梯度提升(XGBoost)模型，给出了一种连续视听刺激下脑电(EEG)情感四分类识别算法。为体现适普性，文中使用传统的功率谱密度(PSD)作为脑电信号特征，并结合XGBoost学习得到weight指标下的特征重要性度量，然后使用线性核的主成分分析对经阈值选择的重要特征进行处理后送入XGBoost模型进行识别。通过实验分析，gamma频段在XGBoost模型识别的参与重要度明显高于其他频段；另外，从通道分布上看，中央、顶叶和右枕区相对于其他脑区发挥着较为重要的作用。本文算法在所有被试参与(SAP)和被试单独依赖(SSD)两种识别方案下的识别准确率分别达到78.4%和92.6%，相对其他文献的识别算法取得了较大的提升。本文提出的方案有助于改善视听激励下脑机情感系统的识别性能。
- 极端梯度提升 /
- 线性核主成分分析 /
- 脑电信号 /
- 情感识别
Abstract: The principal component analysis of linear kernel and XGBoost models are introduced to design electroencephalogram (EEG) classification algorithm of four emotional states under continuous audio-visual stimulation. In order to reflect universality, the traditional power spectral density (PSD) is used as the feature of EEG signal, and the feature importance measure under the weight index is obtained with XGBoost learning. Then linear kernel principal component analysis is used to process the threshold selected features and send them to XGBoost model for recognition. According to the experimental analysis, gamma-band plays a more important role than other bands in XGBoost model recognition; in addition, for distribution on channels, the central, parietal, and right occipital regions play a more important role than other brain regions. The recognition accuracy of this algorithm is 78.4% and 92.6% respectively under the two recognition schemes of subjects all participation (SAP) and subject single dependent (SSD). Compared with other literature, this algorithm has made a great improvement. The scheme proposed is helpful to improve the recognition performance of brain-computer emotion system under audio-visual stimulation.
- eXtreme gradient boosting /
- linear kernel principal component analysis /
- EEG /
- emotion recognition

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图 1 算法整体流程图

Figure 1. Overall flow chart for the algorithm

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图 2 SAP下准确率在不同阈值下的趋势

Figure 2. Accuracy under different thresholds in SAP

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图 3 SAP情况下特征重要性排序

Figure 3. Ranking of feature importance in SAP

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图 4 脑电信号整体分布和特征重要性分布图

Figure 4. Overall distribution and feature importance distribution of EEG signals

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图 5 部分被试(01，04，22，23)特征重要性排序

Figure 5. Features' importance ranking for selected subjects(01, 04, 22, 23)

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图 6 32名被试的识别准确度对比

Figure 6. Comparison of recognition accuracy for 32 subjects

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图 7 不同方法下主成分个数的识别性能对比

Figure 7. Recognition performance comparison for different components

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表 1 各种算法识别效果的比较

Table 1. Comparison of recognition performance for various algorithms

Algorithm	SAP		SSD
Algorithm	Acc/%	f1_weighted/%	Acc/%	f1_weighted/%
SVM^[33]	——	——	57.6/62.0(V/A)	—
XGB	56.832	56.499	57.423	52.822
SVM	52.360	51.152	50.970	43.315
MLP	52.742	51.800	48.642	39.806
RF	58.799	58.243	55.394	51.996
LR	40.911	38.508	55.767	52.307
PCA+SVM^[17]	——	——	68.3	——
SAE+LSTM^[21]	76.82	——	——	——
Lasso+SVM^[23]	——	——	87.15/86.60(V/A)	——
DE+GELM^[19]	69.67	——	——	——
Ours	78.376	77.848	92.583	92.539

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