基于條件經(jīng)驗(yàn)?zāi)Ｊ椒纸夂痛⑿蠧NN的腦電信號(hào)識(shí)別

唐賢倫; 李偉; 馬偉昌; 孔德松; 馬藝瑋

doi:10.11999/JEIT190124

基于條件經(jīng)驗(yàn)?zāi)Ｊ椒纸夂痛⑿蠧NN的腦電信號(hào)識(shí)別

doi: 10.11999/JEIT190124

重慶郵電大學(xué)自動(dòng)化學(xué)院重慶 400065

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61673079, 61703068)，重慶市基礎(chǔ)研究與前沿探索項(xiàng)目(cstc2018jcyjAX0160)

詳細(xì)信息

作者簡(jiǎn)介:
唐賢倫：男，1977年生，教授，博士，研究方向?yàn)橹悄芟到y(tǒng)與機(jī)器人，模式識(shí)別理論與應(yīng)用

李偉：男，1996年生，碩士生，研究方向?yàn)樯疃葘W(xué)習(xí)、腦電信號(hào)識(shí)別

馬偉昌：男，1995年生，碩士生，研究方向?yàn)闄C(jī)器人控制

孔德松：男，1993年生，碩士生，研究方向?yàn)樯疃葘W(xué)習(xí)

馬藝瑋：女，1980年生，副教授，博士，研究方向?yàn)橹悄苄畔⑻幚?/p>

通訊作者:
李偉　cqyddxliwei@foxmail.com

中圖分類號(hào): TP391.4
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-03-01
- 修回日期: 2019-11-22
- 網(wǎng)絡(luò)出版日期: 2019-12-14
- 刊出日期: 2020-06-04

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

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)

摘要

摘要:
針對(duì)運(yùn)動(dòng)想象腦電信號(hào)(EEG)的非線性、非平穩(wěn)特點(diǎn)，該文提出一種結(jié)合條件經(jīng)驗(yàn)?zāi)Ｊ椒纸?CEMD)和串并行卷積神經(jīng)網(wǎng)絡(luò)(SPCNN)的腦電信號(hào)識(shí)別方法。在CEMD過程中，采用各階固有模式分量(IMF)與原始信號(hào)的相關(guān)性系數(shù)作為第1個(gè)IMF篩選條件，在此基礎(chǔ)上，提出各階IMF之間的相對(duì)能量占有率作為第2個(gè)IMF篩選條件。此外，為了考慮腦電信號(hào)各個(gè)通道之間的特征和突出每個(gè)通道內(nèi)的特征，該文提出SPCNN網(wǎng)絡(luò)模型對(duì)進(jìn)行CEMD過程后的腦電信號(hào)進(jìn)行分類。實(shí)驗(yàn)結(jié)果表明，在自行采集的腦電數(shù)據(jù)集上平均識(shí)別率達(dá)到94.58%。在公開數(shù)據(jù)集BCI competition IV 2b上平均識(shí)別率達(dá)到82.13%，比卷積神經(jīng)網(wǎng)絡(luò)提高了3.85%。最后，在自行設(shè)計(jì)的智能輪椅腦電控制平臺(tái)上進(jìn)行了輪椅前進(jìn)、左轉(zhuǎn)和右轉(zhuǎn)在線控制實(shí)驗(yàn)，驗(yàn)證了該文算法對(duì)腦電信號(hào)識(shí)別的有效性。
- 腦電信號(hào)識(shí)別 /
- 經(jīng)驗(yàn)?zāi)Ｊ椒纸?/a> /
- 卷積神經(jīng)網(wǎng)絡(luò) /
- 特征提取 /
- 智能輪椅
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

HTML全文

圖 1 串并行卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)圖

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圖 2 Emotiv腦電采集儀

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圖 3 Emotiv腦電采集儀電極安放位置

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圖 4 單次腦電信號(hào)采集過程

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圖 5 設(shè)定不同的閾值$\alpha $時(shí)識(shí)別率情況

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圖 6 設(shè)定不同的閾值$\beta $時(shí)識(shí)別率情況

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圖 7 采用不同處理方法的識(shí)別準(zhǔn)確率對(duì)比

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圖 8 智能輪椅系統(tǒng)結(jié)構(gòu)圖

下載: 全尺寸圖片幻燈片

表 1 不同算法對(duì)5受試者腦電信號(hào)的識(shí)別準(zhǔn)確率(%)

算法	CSP	ACSP	DBN	CNN	STFT-CNN	SPCNN	本文CEMD-SPCNN
S01	65.00	77.50	87.08	86.25	88.75	90.42	93.33
S02	81.67	82.92	87.50	87.92	89.17	91.25	94.17
S03	98.33	97.08	95.83	95.83	96.67	97.08	99.16
S04	76.25	78.33	83.33	85.42	85.42	86.25	89.58
S05	95.42	96.25	93.75	91.67	92.50	94.17	96.67
均值	83.33	86.41	89.50	89.42	90.50	91.83	94.58
方差	190.01	91.88	26.51	18.61	18.18	16.62	13.02

下載: 導(dǎo)出CSV

表 2 不同算法對(duì)BCI competition IV 2b數(shù)據(jù)集的識(shí)別準(zhǔn)確率(%)

算法	Chin	Gan	Coyle	CSP	ACSP	DBN	CNN	STFT-CNN	SPCNN	本文CEMD-SPCNN
B01	70.00	71.00	60.00	66.56	67.50	66.56	72.22	75.00	76.39	80.56
B02	61.00	61.00	56.00	57.81	55.31	62.50	61.03	61.76	63.24	64.71
B03	61.00	57.00	56.00	61.25	62.19	60.00	61.11	62.50	62.50	64.58
B04	98.00	97.00	89.00	94.06	94.69	96.87	98.65	98.65	99.32	99.32
B05	93.00	86.00	79.00	80.63	76.88	82.19	86.48	87.16	87.84	88.51
B06	81.00	81.00	75.00	75.00	75.94	77.50	79.17	80.56	81.25	83.33
B07	78.00	81.00	69.00	72.50	71.25	76.56	78.47	77.08	79.17	81.25
B08	93.00	92.00	93.00	89.38	89.38	88.75	86.18	86.18	86.84	90.13
B09	87.00	89.00	73.00	85.63	81.25	85.94	81.25	82.64	84.03	86.81
均值	80.22	79.44	72.22	75.86	74.93	77.43	78.28	79.06	80.06	82.13
方差	192.19	190.03	181.69	158.75	157.50	155.85	147.92	138.63	137.52	129.78

下載: 導(dǎo)出CSV

表 3 各類操作在線識(shí)別準(zhǔn)確率(%)

操作	直行	左轉(zhuǎn)	右轉(zhuǎn)
S01	96	84	86
S02	94	90	82
S03	98	88	92

下載: 導(dǎo)出CSV

參考文獻(xiàn)(21)

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, 陳希豪, 高發(fā)榮, 等. 基于感興趣腦區(qū)LASSO-Granger因果關(guān)系的腦電特征提取算法[J]. 電子與信息學(xué)報(bào), 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.

陳強(qiáng), 陳勛, 余鳳瓊. 基于獨(dú)立向量分析的腦電信號(hào)中肌電偽跡的去除方法[J]. 電子與信息學(xué)報(bào), 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, M?LLER 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

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