Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition

Xianlun TANG; Wei LI; Weichang MA; Desong KONG; Yiwei MA

doi:10.11999/JEIT190124

Volume 42 Issue 4

Jun. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(4): 1041-1048

Xianlun TANG, Wei LI, Weichang MA, Desong KONG, Yiwei MA. Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1041-1048. doi: 10.11999/JEIT190124

Citation:

Xianlun TANG, Wei LI, Weichang MA, Desong KONG, Yiwei MA. Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1041-1048. doi: 10.11999/JEIT190124

Citation:

PDF( 1576 KB)

Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition

doi: 10.11999/JEIT190124

School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61673079, 61703068), The Basic Research and Frontier Exploration Project of Chongqing (cstc2018jcyjAX0160)

Received Date: 2019-03-01
Rev Recd Date: 2019-11-22

Available Online: 2019-12-14

Publish Date: 2020-06-04

Abstract

Abstract

For the non-linear and non-stationary characteristics of motor imagery ElectroEncephaloGram (EEG) signals, an EEG signal recognition method based on Conditional Empirical Mode Decomposition (CEMD) and Serial Parallel Convolutional Neural Network (SPCNN) is proposed. In the CEMD process, the correlation coefficient between the Intrinsic Mode Functions (IMFs) and the original signal is used as the first condition to select IMFs. Based on this, the relative energy occupancy rates between the IMFs are proposed as the second condition to select IMFs. Further, to consider the characteristics between the EEG signal channels and highlight the features in each EEG signal channel, a SPCNN model is proposed to classify the processed EEG signals. The experimental results show that the average recognition rate reaches 94.58% on the dataset collected by ourselves. And the average recognition rate reaches 82.13% on the BCI competition IV 2b dataset, which is 3.85% higher than the average recognition rate of convolutional neural network. Finally, the online control experiments are carried out on the designed intelligent wheelchair platform, which proves the effectiveness of the proposed algorithm for EEG signals recognition.
- Electro Encephalo Gram(EEG) recognition,
- Empirical Mode Decomposition (EMD),
- Convolutional Neural Network (CNN),
- Feature extraction,
- Intelligent wheelchair

FullText(HTML)

References(21)

References

RAMADAN R A and VASILAKOS A V. Brain computer interface: Control signals review[J]. Neurocomputing, 2017, 223: 26–44. doi: 10.1016/j.neucom.2016.10.024

佘青山, 陈希豪, 高发荣, 等. 基于感兴趣脑区LASSO-Granger因果关系的脑电特征提取算法[J]. 电子与信息学报, 2016, 38(5): 1266–1270. doi: 10.11999/JEIT150851

SHE Qingshan, CHEN Xihao, GAO Farong, et al. Feature extraction of electroencephalography based on LASSO-Granger causality between brain region of interest[J]. Journal of Electronics &Information Technology, 2016, 38(5): 1266–1270. doi: 10.11999/JEIT150851

YU Tianyou, XIAO Jun, WANG Fangyi, et al. Enhanced motor imagery training using a hybrid BCI with feedback[J]. IEEE Transactions on Biomedical Engineering, 2015, 62(7): 1706–1717. doi: 10.1109/TBME.2015.2402283

KAUFMANN T, HERWEG A, and KÜBLER A. Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials[J]. Journal of NeuroEngineering and Rehabilitation, 2014, 11: No.7. doi: 10.1186/1743-0003-11-7

WANG Haofei, DONG Xujiong, CHEN Zhaokang, et al. Hybrid gaze/EEG brain computer interface for robot arm control on a pick and place task[C]. The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 2015: 1476–1479. doi: 10.1109/EMBC.2015.7318649.

陈强, 陈勋, 余凤琼. 基于独立向量分析的脑电信号中肌电伪迹的去除方法[J]. 电子与信息学报, 2016, 38(11): 2840–2847. doi: 10.11999/JEIT160209

CHEN Qiang, CHEN Xun, and YU Fengqiong. Removal of muscle artifact from EEG data based on independent vector analysis[J]. Journal of Electronics &Information Technology, 2016, 38(11): 2840–2847. doi: 10.11999/JEIT160209

HSU W Y. EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features[J]. Journal of Neuroscience Methods, 2010, 189(2): 295–302. doi: 10.1016/j.jneumeth.2010.03.030

WU Shanglin, WU Chunwei, PAL N R, et al. Common spatial pattern and linear discriminant analysis for motor imagery classification[C]. 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Singapore, 2013: 146–151. doi: 10.1109/CCMB.2013.6609178.

ZHANG Yu, WANG Yu, ZHOU Guoxu, et al. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces[J]. Expert Systems with Applications, 2018, 96: 302–310. doi: 10.1016/j.eswa.2017.12.015

KEVRIC J and SUBASI A. Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system[J]. Biomedical Signal Processing and Control, 2017, 31: 398–406. doi: 10.1016/j.bspc.2016.09.007

PARK C, LOONEY D, REHMAN N U, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(1): 10–22. doi: 10.1109/TNSRE.2012.2229296

DOSE H, MØLLER J S, IVERSEN H K, et al. An end-to-end deep learning approach to MI-EEG signal classification for BCIs[J]. Expert Systems with Applications, 2018, 114: 532–542. doi: 10.1016/j.eswa.2018.08.031

ZHANG Jin, YAN Chungang, and GONG Xiaoliang. Deep convolutional neural network for decoding motor imagery based brain computer interface[C]. 2017 IEEE International Conference on Signal Processing, Communications and Computing, Xiamen, China, 2017: 1–5. doi: 10.1109/ICSPCC.2017.8242581.

AN Xiu, KUANG Deping, GUO Xiaojiao, et al. A deep learning method for classification of EEG data based on motor imagery[C]. The 10th International Conference on Intelligent Computing, Taiyuan, China, 2014: 203–210. doi: 10.1007/978-3-319-09330-7_25.

LI Shufang, ZHOU Weidong, YUAN Qi, et al. Feature extraction and recognition of ictal EEG using EMD and SVM[J]. Computers in Biology and Medicine, 2013, 43(7): 807–816. doi: 10.1016/j.compbiomed.2013.04.002

TARAN S, BAJAJ V, SHARMA D, et al. Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications[J]. Measurement, 2018, 116: 68–76. doi: 10.1016/j.measurement.2017.10.067

MU Yashuang, LIU Xiaodong, and WANG Lidong. A Pearson’s correlation coefficient based decision tree and its parallel implementation[J]. Information Sciences, 2018, 435: 40–58. doi: 10.1016/j.ins.2017.12.059

LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278–2324. doi: 10.1109/5.726791

SAKHAVI S, GUAN Cuntai, and YAN Shuicheng. Learning temporal information for brain-computer interface using convolutional neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(11): 5619–5629. doi: 10.1109/TNNLS.2018.2789927

SUN Shiliang and ZHOU Jin. A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces[C]. 2014 International Joint Conference on Neural Networks, Beijing, China, 2014: 1746–1753. doi: 10.1109/IJCNN.2014.6889525.

LU Na, LI Tengfei, REN Xiaodong, et al. A deep learning scheme for motor imagery classification based on restricted Boltzmann machines[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(6): 566–576. doi: 10.1109/TNSRE.2016.2601240

Relative Articles

Supplements(0)

Cited By

Proportional views