Applications of Wavelet Transform Combining Empirical Mode Decomposition in EEG Analysis with Music Intervention_Journal of Biomedical Engineering

Authors：

1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China;
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao 066004, China;
3. College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China;
4. Qinhuangdao Prospect Photoeletric Tech Co., Ltd, Qinghuangdao 066004, China;

Corresponding author：

Keywords：

music therapy; electroencephalogram; wavelet transform; empirical mode decomposition; intrinsic mode function

DOI：

10.7507/1001-5515.20160124

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In the present paper, wavelet transform and empirical mode decomposition (EMD) are combined to extracted the features of electroencephalogram (EEG) signal with music intervention, and to achieve a better classification accuracy rate and reliability in emotional assessment in order to provide a support for music therapy. The data were from Database for Emotion Analysis using Physiological Signals (DEAP). Based on wavelet transform α, β and θ rhythms were extracted at frontal (F3, F4), temporal (T7, T8) and central regions (C3, C4). Based on the EMD, the intrinsic mode function (IMF) was analyzed and extracted. Furthermore, average energy and amplitude difference of IMF were analyzed and obtained. The support vector machine was used to assess the state of emotion in order to support music therapy. According to this algorithm, the classification accuracy rate could reach 100% between no emotions, positive emotions and negative emotions, which made a 10% improvement between positive and negative emotion recognition. Effective evaluation result between positive and negative emotions was achieved. The states of emotion would influence the effect of music therapy, undoubtedly, the classification accuracy rate increasing of emo-tional assessment will further help improve the effect of music therapy and provide a better support to the therapy.

Citation： LIXin, TIANYanxiu, HOUYongjie, QIXiaoying, SUNXiaoqi, FANMengdi, CAIErjuan. Applications of Wavelet Transform Combining Empirical Mode Decomposition in EEG Analysis with Music Intervention. Journal of Biomedical Engineering, 2016, 33(4): 762-769. doi: 10.7507/1001-5515.20160124 Copy

1.	陈蕾.浅谈音乐治疗及其应用[J].西南大学学报:社会科学版, 2010, 36(3):207-208.
2.	黄晓莹, 黄涛.音乐治疗在康复医学中的应用[J].中国临床康复, 2004, 8(22):4578-4579.
3.	李哲.中西方古典音乐对记忆的影响——春江花月夜曲与莫扎特D大调双钢琴奏鸣曲K.448[D].重庆:西南大学, 2009.
4.	KARTHICK N G, AHAMED V I T, PAUL J K. Music and the EEG:A study using nonlinear methods[C]//Internationl Conference on Biomedical and Pharmaceutical Engineering, 2006. Singapore:2006:424-427.
5.	MIKUTTA C, ALTORFER A, STRIK W, et al. Emotions, arousal, and frontal alpha rhythm asymmetry during Beethoven's 5th symphony[J]. Brain Topogr, 2012, 25(4):423-430.
6.	KABUTO M, KAGEYAMA T, NITTA H. EEG power spectrum changes due to listening to pleasant music and their relation to relaxation effects[J]. Nihon Eiseigaku Zasshi, 1993, 48(4):807-818.
7.	王小甜.不同情绪状态下脑电信号特征的研究[D].长春:长春理工大学, 2014.
8.	赖永秀, 高婷婷, 吴丹, 等.音乐情绪感知的脑电研究[J].电子科技大学学报, 2008, 37(2):301-304.
9.	陈曾, 刘光远.脑电信号在情感识别中的应用[J].计算机工程, 2010, 36(9):168-170.
10.	杨航, 郭晓金.基于EMD的运动想象脑电特征提取与识别[J].软件导刊, 2015, 14(1):44-47.
11.	HUANG N E, SHEN Z, LONG S L, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of Conference on Mathematical, Physical and Engineering Sciences, London:The Royal Society, 1998, 454:903-995.
12.	陈一贤. HHT方法分析[D].杭州:浙江大学, 2007.
13.	谢平, 陈晓玲, 苏玉萍, 等.基于EMD-多尺度熵和ELM的运动想象脑电特征提取和模式识别[J].中国生物医学工程学报, 2013, 32(6):641-648.
14.	余炜, 韩强, 马晶晶, 等.基于EMD和SVM的脑电信号处理方法[J].昆明理工大学学报:自然科学版, 2012, 37(6):38-42.
15.	DIEZ P F, MUT V, LACIAR E. Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification[C]//Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis, MN:2009:2579-2582.
16.	PARVEZ M Z, PAUL M. Features Extraction and Classification for Ictal and Interictal EEG Signals using EMD and DCT[C]//2012 15th International Conference on Computer and Information Technology (ICCIT). Chittagong:2012:132-137.
17.	ARASTEH A, JANGHORBANI A, MORADI M H. A Survey on EMD Sensitivity to SNR for EEG Feature Extraction in BCI Application[C]//2010 5th Cairo International Biomedical Engineering Conference (CIBEC). Cairo:2010:175-179.
18.	杨鹏圆.基于小波包和Hilbert-Huang变换的情感脑电识别研究[D].太原:太原理工大学, 2014.
19.	KOELSTRA S, MUHL C, SOLEYMANI M, et al. DEAP:A database for emotion analysis using physiological signals[J]. IEEE Trans Affective Computing, 2012, 3(1):18-31.
20.	张德乾, 刘健, 林梦.改变音乐节拍对脑电功率谱变异的影响及其心理意义[J].绵阳师范学院学报, 2014, 33(2):56-62.
21.	谢康.情绪音乐的脑电识别算法[D].成都:电子科技大学, 2013.
22.	侯建成, 刘昌.调式和速度组成的不同音乐形态诱发情绪活动的脑电研究[J].心理学探新, 2012, 30(6):86-93.
23.	袁媛.音乐速度感知的脑电研究[D].成都:电子科技大学, 2009.
24.	毛茅.音乐诱发情绪的心理生理测量及其在服务设计中的应用[D].北京:清华大学, 2013.
25.	高婷婷.音乐情绪感知的脑电研究[D].成都:电子科技大学, 2007.
26.	董光恒.积极情绪与消极情绪启动对冲动控制影响的差异研究[D].大连:辽宁师范大学, 2008.
27.	郭小艳, 王振宏.积极情绪的概念、功能与意义[J].心理科学进展, 2007, 15(5):810-815.
28.	汤华丽. SVM中两类常用分类方法的关系研究[D].重庆:重庆大学, 2005.
29.	范昕炜.支持向量机算法的研究及其应用[D].杭州:浙江大学, 2003.
30.	RAY W J, COLE H W. EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes[J]. Science, 1985, 228(4700):750-752.
31.	黄思娟, 吴效明.基于能量特征的脑电信号特征提取与分类[J].传感技术学报, 2010, 23(6):782-785.

1. 陈蕾.浅谈音乐治疗及其应用[J].西南大学学报:社会科学版, 2010, 36(3):207-208.
2. 黄晓莹, 黄涛.音乐治疗在康复医学中的应用[J].中国临床康复, 2004, 8(22):4578-4579.
3. 李哲.中西方古典音乐对记忆的影响——春江花月夜曲与莫扎特D大调双钢琴奏鸣曲K.448[D].重庆:西南大学, 2009.
4. KARTHICK N G, AHAMED V I T, PAUL J K. Music and the EEG:A study using nonlinear methods[C]//Internationl Conference on Biomedical and Pharmaceutical Engineering, 2006. Singapore:2006:424-427.
5. MIKUTTA C, ALTORFER A, STRIK W, et al. Emotions, arousal, and frontal alpha rhythm asymmetry during Beethoven's 5th symphony[J]. Brain Topogr, 2012, 25(4):423-430.
6. KABUTO M, KAGEYAMA T, NITTA H. EEG power spectrum changes due to listening to pleasant music and their relation to relaxation effects[J]. Nihon Eiseigaku Zasshi, 1993, 48(4):807-818.
7. 王小甜.不同情绪状态下脑电信号特征的研究[D].长春:长春理工大学, 2014.
8. 赖永秀, 高婷婷, 吴丹, 等.音乐情绪感知的脑电研究[J].电子科技大学学报, 2008, 37(2):301-304.
9. 陈曾, 刘光远.脑电信号在情感识别中的应用[J].计算机工程, 2010, 36(9):168-170.
10. 杨航, 郭晓金.基于EMD的运动想象脑电特征提取与识别[J].软件导刊, 2015, 14(1):44-47.
11. HUANG N E, SHEN Z, LONG S L, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of Conference on Mathematical, Physical and Engineering Sciences, London:The Royal Society, 1998, 454:903-995.
12. 陈一贤. HHT方法分析[D].杭州:浙江大学, 2007.
13. 谢平, 陈晓玲, 苏玉萍, 等.基于EMD-多尺度熵和ELM的运动想象脑电特征提取和模式识别[J].中国生物医学工程学报, 2013, 32(6):641-648.
14. 余炜, 韩强, 马晶晶, 等.基于EMD和SVM的脑电信号处理方法[J].昆明理工大学学报:自然科学版, 2012, 37(6):38-42.
15. DIEZ P F, MUT V, LACIAR E. Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification[C]//Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis, MN:2009:2579-2582.
16. PARVEZ M Z, PAUL M. Features Extraction and Classification for Ictal and Interictal EEG Signals using EMD and DCT[C]//2012 15th International Conference on Computer and Information Technology (ICCIT). Chittagong:2012:132-137.
17. ARASTEH A, JANGHORBANI A, MORADI M H. A Survey on EMD Sensitivity to SNR for EEG Feature Extraction in BCI Application[C]//2010 5th Cairo International Biomedical Engineering Conference (CIBEC). Cairo:2010:175-179.
18. 杨鹏圆.基于小波包和Hilbert-Huang变换的情感脑电识别研究[D].太原:太原理工大学, 2014.
19. KOELSTRA S, MUHL C, SOLEYMANI M, et al. DEAP:A database for emotion analysis using physiological signals[J]. IEEE Trans Affective Computing, 2012, 3(1):18-31.
20. 张德乾, 刘健, 林梦.改变音乐节拍对脑电功率谱变异的影响及其心理意义[J].绵阳师范学院学报, 2014, 33(2):56-62.
21. 谢康.情绪音乐的脑电识别算法[D].成都:电子科技大学, 2013.
22. 侯建成, 刘昌.调式和速度组成的不同音乐形态诱发情绪活动的脑电研究[J].心理学探新, 2012, 30(6):86-93.
23. 袁媛.音乐速度感知的脑电研究[D].成都:电子科技大学, 2009.
24. 毛茅.音乐诱发情绪的心理生理测量及其在服务设计中的应用[D].北京:清华大学, 2013.
25. 高婷婷.音乐情绪感知的脑电研究[D].成都:电子科技大学, 2007.
26. 董光恒.积极情绪与消极情绪启动对冲动控制影响的差异研究[D].大连:辽宁师范大学, 2008.
27. 郭小艳, 王振宏.积极情绪的概念、功能与意义[J].心理科学进展, 2007, 15(5):810-815.
28. 汤华丽. SVM中两类常用分类方法的关系研究[D].重庆:重庆大学, 2005.
29. 范昕炜.支持向量机算法的研究及其应用[D].杭州:浙江大学, 2003.
30. RAY W J, COLE H W. EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes[J]. Science, 1985, 228(4700):750-752.
31. 黄思娟, 吴效明.基于能量特征的脑电信号特征提取与分类[J].传感技术学报, 2010, 23(6):782-785.

Journal of Biomedical Engineering

Applications of Wavelet Transform Combining Empirical Mode Decomposition in EEG Analysis with Music Intervention

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