基于LSTM-Attention的P300事件相关电位识别分类研究

doi:10.16180/j.cnki.issn1007-7820.2022.12.002

Abstract

Abstract:

In view of the problem of low recognition and classification accuracy of P300 event-related potentials in EEG signals, a recognition and classification method of P300 event-related potentials based on LSTM-Attention network is proposed in this study. In the data processing stage, SMOTE is utilized to augment P300 potential data in EEG signals, and irrelevant noise in synthetic data is eliminated based on DBSCAN clustering algorithm. In the identification and classification stage, an LSTM-Attention classification and identification network is built by adding an attention mechanism and a Dropout layer after the LSTM network, and the Sigmoid function is used to output the identification and classification results of the P300 event-related potential. The experimental results show that the proposed method can effectively recognize and classify P300 event-related potentials in EEG signals, and the average accuracy and Dice coefficient are up to 91.9% and 91.7%, respectively. Compared with traditional methods, the accuracy is higher and the generalization performance is stronger.

Key words: P300 event-related potential, EEG, LSTM, attention mechanism, data processing, SMOTE, DBSCAN, recognition and classification

CLC Number:

TP391

WANG Xialin,KAN Xiu,FAN Yixuan. Classification and Recognition of P300 Event-Related Potential Based on LSTM-Attention Network[J].Electronic Science and Technology, 2022, 35(12): 10-16.

Figures/Tables 11

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Table 1.

Table 2.

Table 3.

Figure 8.

References 18

[1]	孟小飞. P300脑电信号的特征提取与分类研究[D]. 杭州: 杭州电子科技大学, 2020.
	Meng Xiaofei. Feature extraction and classification of P300 signal[D]. Hangzhou: Hangzhou Dianzi University, 2020.
	孔丽文, 薛召军, 陈龙, 等. 基于虚拟现实环境的脑机接口技术研究进展[J]. 电子测量与仪器学报, 2015, 29(3):317-327.
	Kong Liwen, Xue Zhaojun, Chen Long, et al. Review of brain-computer interface technology based on virtual reality environment[J]. Journal of Electronic Measurement and Instrumentation, 2015, 29(3):317-327.
[2]	王根, 方慧娟, 罗登. 基于事件相关电位的BCI新型输入系统研究[J]. 信息技术与网络安全, 2013, 32(7):66-68.
	Wang Gen, Fang Huijuan, Luo Deng. Study on the new input of BCI system based on ERP[J]. Information Technology and Network Security, 2013, 32(7):66-68.
[3]	闫秘. 基于P300-EEG分类的多域融合技术研究[D]. 长春: 长春理工大学, 2020.
	Yan Mi. Research on multi-domain fusion technology based on P300 EEG classification[D]. Changchun: Changchun University of Science and Technology, 2020.
[4]	马征, 邱天爽. 视觉ERP脑机接口中实验范式的研究进展[J]. 中国生物医学工程学报, 2016, 35(1):96-104.
	Ma Zheng, Qiu Tianshuang. A review of experimental paradigms in visual event-related potential-based brain computer interfaces[J]. Chinese Journal of Biomedical Engineering, 2016, 35(1):96-104.
[5]	Bostanov V. BCI competition 2003-data sets Ib and IIb:Feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(6):1057-1061. doi: 10.1109/TBME.2004.826702
[6]	Xu N, Gao X R, Hong B, et al. BCI Competition 2003-data set IIb: Enhancing P300 wave detection using ICA-based subspace projections for BCI applications[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(6):1067-1072. doi: 10.1109/TBME.2004.826699
[7]	潘家辉, 冯宝. 脑机接口中脑电信号的特征提取和模式分类[J]. 计算机系统应用, 2015, 24(8):268-272.
	Pan Jiahui, Feng Bao. Feature extraction and pattern classification for EEG in brain-computer interface[J]. Computer Systems and Applications, 2015, 24(8):268-272.
[8]	Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: Synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002(16):321-357.
[10]	Farwell L A, Donchin E. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials[J]. Electroencephalography & Clinical Neurophysiology, 1988, 70(6):510-523.
[11]	Blankertz B, Müller K R, Krusienski D J, et al. The BCI competition III: Validating alternative approaches to actual BCI problems[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14(2):153-159. pmid: 16792282
[12]	贺玲, 吴玲达, 蔡益朝. 数据挖掘中的聚类算法综述[J]. 计算机应用研究, 2007(1):10-13.
	He Ling, Wu Lingda, Cai Yichao. Survey of clustering algorithms in data mining[J]. Application Research of Computers, 2007(1):10-13.
[13]	祁鑫, 王福忠, 张丽, 等. 基于SVD-LSTM的高校学生宿舍空调负荷预测[J]. 电子科技, 2020, 33(11):59-66.
	Qi Xin, Wang Fuzhong, Zhang Li, et al. Air conditioning load forecast of university students' dormitory based on SVD-LSTM[J]. Electronic Science and Technology, 2020, 33(11):59-66.
[14]	刘嘉琛, 秦小麟, 朱润泽. 基于LSTM-Attention的RFID移动对象位置预测[J]. 计算机科学, 2021, 48(3):188-195. doi: 10.11896/jsjkx.200600134
	Liu Jiachen, Qin Xiaolin, Zhu Runze. Prediction of RFID mobile object location based on LSTM-Attention[J]. Computer Science, 2021, 48(3):188- 195. doi: 10.11896/jsjkx.200600134
[15]	葛靖, 刘子龙. 基于CNN和LSTM的睡眠呼吸暂停检测算法[J]. 电子科技, 2021, 34(2):21-26.
	Ge Jing, Liu Zilong. The algorithm based on CNN and LSTM for sleep apnea syndrome detection[J]. Electronic Science and Technology, 2021, 34(2):21-26.
[16]	Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[17]	魏华栋, 陶媛, 蔡昌春, 等. 基于改进长短期记忆神经网络的短期负荷预测[J]. 电测与仪表, 2020, 57(19):93-98.
	Wei Huadong, Tao Yuan, Cai Changchun, et al. Short-term load forecasting based on improved long short-term memory neural network[J]. Electrical Measurement & Instrumentation, 2020, 57(19):93-98.
[18]	赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12):4370-4376.
	Zhao Bing, Wang Zengping, Ji Weijia, et al. A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power System Technology, 2019, 43(12): 4370-4376.

网络	Dice系数/%	准确率/%
SVM	75.4	76.8
RNN	82.1	82.4
LSTM-Attention	91.7	91.9

网络	数据增强	Dice系数/%	准确率/%
LSTM		86.3	87.2
LSTM	?	89.1	90.1
LSTM-Attention		90.5	90.9
LSTM-Attention	?	91.7	91.9

[1]	CHEN Jinhong,CHEN Wei,YIN Zhong. Semantic Segmentation of Streetscape Based on Improved ExfuseNet [J]. Electronic Science and Technology, 2022, 35(6): 28-34.
[2]	Chaowei LIN,Feifei LI,Qiu CHEN. Globaland Local Scene Representation Method Based on Deep Convolutional Features [J]. Electronic Science and Technology, 2022, 35(4): 20-27.
[3]	Yanmei YANG,Zongmao CHENG. Prediction of PM_2.5 Based on External Influences and Time-Series Factors [J]. Electronic Science and Technology, 2022, 35(3): 51-57.
[4]	Lingyu JI,Yongbin GAO,Chenglu ZHAO,Xianhua TANG,Kaicheng XU,Jiacheng XU. CTA Segmentation Algorithm of Abdominal Artery Based on 3D Fully Convolutional Network [J]. Electronic Science and Technology, 2022, 35(3): 38-44.
[5]	YU Runrun,JIANG Xiaoyan,ZHU Kaiying,JIANG Guanghao. Real-Time Image Semantic Segmentation Based on Contextual Attention Mechanism [J]. Electronic Science and Technology, 2022, 35(12): 57-63.
[6]	ZHANG Shisen,SUN Xiankun,YIN Ling,LI Shixi. Design of Text Title Generation Prototype System Based on Neural Network [J]. Electronic Science and Technology, 2021, 34(5): 35-41.
[7]	YAN Shuhao,QIAO Meiying. Bearing Fault Diagnosis Algorithm Based on One-Dimensional WConv-BiLSTM [J]. Electronic Science and Technology, 2021, 34(4): 75-82.
[8]	XUE Yongjie,JU Zhiyong. Fish Recognition Algorithm Based on Improved AlexNet [J]. Electronic Science and Technology, 2021, 34(4): 12-17.
[9]	ZHENG Zhanpeng,YIN Zhong. Inter-Subject Recognition of Cognitive Workload Based on Ensemble SDAE and EEG [J]. Electronic Science and Technology, 2021, 34(3): 48-53.
[10]	GE Jing,LIU Zilong. The Algorithm Based on CNN and LSTM for Sleep Apnea Syndrome Detection [J]. Electronic Science and Technology, 2021, 34(2): 21-26.
[11]	LI Fupeng,FU Dongxiang. Sentiment Analysis Method of Financial Text Based on Transformer Encoder [J]. Electronic Science and Technology, 2020, 33(9): 10-15.
[12]	ZHENG Meng. Research on Intelligent English Translation Method Based on Improved Attention Mechanism Model [J]. Electronic Science and Technology, 2020, 33(11): 84-87.
[13]	ZHANG Yue,HU Chunyan. Research on Emotion Recognition of EEG Features Based on the Long Short-term Memory Neural Network [J]. Electronic Science and Technology, 2020, 33(11): 67-72.
[14]	QI Xin,WANG Fuzhong,ZHANG Li,WANG Rui,WANG Xiaohui. Air Conditioning Load Forecast of University Students' Dormitory Based on SVD-LSTM [J]. Electronic Science and Technology, 2020, 33(11): 59-66.
[15]	JIANG Meng,WANG Ziniu,GAO Jianling. Chinese Word Segmentation Based on Joint Training of Heterogeneous Data [J]. Electronic Science and Technology, 2019, 32(4): 29-33.

Classification and Recognition of P300 Event-Related Potential Based on LSTM-Attention Network

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 18

Related Articles 15

Metrics

Comments

Recommended 0