Tổng Quan Hệ Thống Về Phân Loại Cảm Xúc Dựa Trên Tín Hiệu Não EEG Của Con Người, Trích Xuất Đặc Trưng, Tình Trạng Não, So Sánh Nhóm

Lin, Y. P., Yang, Y. H., and Jung, T. P., Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening. Front. Neurosci. 8:1–14, 2014. Chavan, D. R., Kumbhar, M. S., and Chavan, R. R., The human stress recognition of brain, using music therapy. 2016 Int. Conf. Comput. Power, Energy, Inf. Commun. ICCPEIC 2016, pp. 200–203, 2016. Ogawa, T., Karungarul, S., Mitsukura, Y., Fukumil, M., and Akamatsul, N., Feature extraction in listen ’ ng to Mus ;’ C using statistical analystis of the EEG. no. D, pp. 5120–5123, 2006. Naji, M., Firoozabadi, M., and Azadfallah, P., Emotion classification during music listening from forehead biosignals. Signal, Image Video Process. 9(6):1365–1375, 2015. Sandler, H., Tamm, S., Fendel, U., Rose, M., Klapp, B. F., and Bösel, R., Positive Emotional Experience: Induced by Vibroacoustic Stimulation Using a Body Monochord in Patients with Psychosomatic Disorders: Is Associated with an Increase in EEG-Theta and a Decrease in EEG-Alpha Power. Brain Topogr. 29(4):524–538, 2016. Chang, Y.-H., Lee, Y.-Y., Liang, K.-C., Chen, I.-P., Tsai, C.-G., and Hsieh, S., Experiencing affective music in eyes-closed and eyes-open states: an electroencephalography study. Front. Psychol. 6:1160–1168, 2015. Kemalasari ve Purnomo, M. H., Analysis the dominant location of brain activity in frontal lobe using K-means method. Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng. 2009, ICICI-BME 2009, pp. 8–10, 2009. Lin, Y. P., Wang, C. H., Wu, T. L., Jeng, S. K., and Chen, J. H., Multilayer perceptron for EEG signal classification during listening to emotional music. IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, 2007. Murugappan, M., and Murugappan, S., Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT). Signal Process. its Appl. (CSPA), 2013 IEEE 9th Int. Colloq., pp. 289–294, 2013. Morita Y., Huang, H. H., and Kawagoe, K., Towards music information retrieval driven by EEG signals: Architecture and preliminary experiments. 2013 IEEE/ACIS 12th Int. Conf. Comput. Inf. Sci. ICIS 2013 - Proc., pp. 213–217, 2013. Bhardwaj, A., Gupta, A., Jain, P., Rani, A., and Yadav, J., Classification of human emotions from EEG signals using SVM and LDA Classifiers. 2015 2nd Int. Conf. Signal Process. Integr. Networks, pp. 180–185, 2015. Daly, I. et al., Identifying music-induced emotions from EEG for use in brain-computer music interfacing. 2015 Int. Conf. Affect. Comput. Intell. Interact. ACII 2015 22:923–929, 2015. Navea, R. F., and Dadios, E., Classification of tone stimulated EEG signals using independent components and power spectrum vectors. 8th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag. HNICEM 2015, no., 2016. Thammasan, N., Application of Deep Belief Networks in EEG-based Dynamic Music-emotion Recognition. pp. 881–888, 2016. Khosrowabadi, R., Wahab, A., and Ang, K. K., From musical and vocal stimuli. Heart:1590–1594, 2009. Kumar, N., Khaund, K., and Hazarika, S. M., Bispectral analysis of EEG for emotion recognition. Procedia Comput. Sci. 84:31–35, 2016. Bhatti, A. M., Majid, M., Anwar, S. M., and Khan, B., Human emotion recognition and analysis in response to audio music using brain signals. Comput. Hum. Behav. 65:267–275, 2016. Chanel, G., Kierkels, J. J. M., Soleymani, M., and Pun, T., Short-term emotion assessment in a recall paradigm. Int. J. Hum. Comput. Stud. 67(8):607–627, 2009. Lin, Y. P. et al., EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7):1798–1806, 2010. Gupta, R., ur Rehman Laghari, K., and Falk, T. H., Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization. Neurocomputing 174:875–884, 2016. Hadjidimitriou, S. K., and Hadjileontiadis, L. J., Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans. Biomed. Eng. 59(12):3498–3510, 2012. Hadjidimitriou, S. K., and Hadjileontiadis, L. J., EEG-Based classification of music appraisal responses using time-frequency analysis and familiarity ratings. IEEE Trans. Affect. Comput. 4(2):161–172, 2013. Yu, G., and Chan, K. C. C., What Strikes the Strings of Your Heart?–Multi-Label Dimensionality Reduction for Music Emotion Analysis via Brain Imaging. IEEE Trans. Auton. Ment. Dev. 7(3):176–188, 2015. Lin, Y., and Jung, T.-P., Exploring day-to-day variability in EEG-based emotion classification. IEEE Int. Conf. Syst. Man, Cybern., pp. 2226–2229, 2014. Murugappan, M., Human emotion classification using wavelet transform and KNN. vol. 1, pp. 148–153, 2011. Ito, S. I., Mitsukura, Y., Sato, K., Fujisawa, S., and Fukumi, M., A study on relationship between personal feature of EEG and human’s characteristic for BCI based on mental state. IECON Proc. (Industrial Electron. Conf., pp. 4229–4232, 2009. Gawali, B. W., Rao, S., Abhang, P., Rokade, P., and Mehrotra, S. C., Classification of Eeg Signals for Different Emotional. Commun. Comput. (ARTCom2012), Fourth Int. Conf. Adv. Recent Technol., pp. 177–181, 2012. Rahnuma, K. S., Wahab, A., Kamaruddin, N., and Majid, H., EEG analysis for understanding stress based on affective model basis function. Proc. Int. Symp. Consum. Electron. ISCE, pp. 592–597, 2011. Nawasalkar, R. K., EEG based stress recognition system based on Indian classical music. 2015. Sreedevi, M., Ajesh, A., Ajithnath, R., and Binu, L. S., A Study of Effect of Music Pitch Variation in EEG Using Factor Analysis and Neural Networks. 2009 2nd Int. Conf. Biomed. Eng. Informatics, pp. 9–11, 2009. Hsu, J.-L., Zhen, Y.-L., Lin, T.-C., and Chiu, Y.-S., Personalized music emotion recognition using electroencephalography (EEG). 2014 IEEE Int. Symp. Multimed., pp. 277–278, 2014. Zhang, F., Meng, H., and Li, M., Emotion extraction and recognition from music. 2016 12th Int. Conf. Nat. Comput. Fuzzy Syst. Knowl. Discov. ICNC-FSKD 2016, pp. 1728–1733, 2016. Tseng, K. C., Lin, B. S., Han, C. M., and Wang, P. S., Emotion recognition of EEG underlying favourite music by support vector machine. ICOT 2013 - 1st Int. Conf. Orange Technol., pp. 155–158, 2013. Bajaj, V. and Pachori, R. B., Human Emotion Classification from EEG Signals Using Multiwavelet Transform. 2014 Int. Conf. Med. Biometrics, no. Md, pp. 125–130, 2014. Jang, D., Park, Y. J., Shin, S., Lee, J., Jang, S. J., and Lim, T. B., Research about relation of music preference and brain-wave. Int. Conf. Inf. Netw. 2015:466–467, 2015. Wang, S., Zhu, Y., Yue, L., and Ji, Q., Emotion recognition with the help of privileged information. IEEE Trans. Auton. Ment. Dev. 7(3):189–200, 2015. Islam, M., Ahmad, M., and Yusuf, M. S. U., An approach to estimate cognitive state with the impact of listening music on brain activity. 2nd Int. Conf. Electr. Inf. Commun. Technol. EICT 2015, no. Eict, pp. 152–157, 2016. Daimi, S. N., and Saha, G., Classification of emotions induced by music videos and correlation with participants’ rating. Expert Syst. Appl. 41(13):6057–6065, 2014. Shahabi, H., and Moghimi, S., Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity. Comput. Hum. Behav. 58:231–239, 2016. Daly, I. et al., Affective brain–computer music interfacing. J. Neural Eng. 13(4):46022, 2016. Jatupaiboon, N., Pan-Ngum, S., and Israsena, P., Subject-Dependent and Subject-Independent Emotion Classification Using Unimodal and Multimodal Physiological Signals. J. Med. Imaging Heal. Informatics 5(5):1020–1027, 2015. Jatupaiboon, N., Pan-Ngum, S., and Israsena, P., Real-time EEG-based happiness detection system. Sci. World J., vol. 2013, 2013. Lin, Y. P., Wang, C. H., Wu, T. L., Jeng, S. K., and Chen, J. H., Support vector machine for EEG signal classification during listening to emotional music. Proc. 2008 IEEE 10th Work. Multimed. Signal Process. MMSP 2008, pp. 127–130, 2008. Ramirez, R., Palencia-Lefler, M., Giraldo, S., and Vamvakousis, Z., Musical neurofeedback for treating depression in elderly people. Front. Neurosci. 9:1–10, 2015. Sourina, O., Liu, Y., and Nguyen, M. K., Real-time EEG-based emotion recognition for music therapy. J. Multimodal User Interfaces 5(1–2):27–35, 2012. Hasminda-Hassan, Z., Murat, H., Ross, V., Mohd-Zain, Z., and Buniyamin, N., Enhancing learning using music to achieve a balanced brain. 2011 3rd Int. Congr. Eng. Educ. Rethink. Eng. Educ. W. Forward, ICEED 2011, pp. 66–70, 2011. Mohd Aris, S. A., Sulaiman, N., Abdul Hamid, N. H., and Taib, M. N., Initial investigation on alpha asymmetry during listening to therapy music, Proc. - CSPA 2010 2010 6th Int. Colloq. Signal Process. Its Appl., pp. 255–258, 2010. Daly, I. et al., Neural correlates of emotional responses to music: An EEG study. Neurosci. Lett. 573:52–57, 2014. Farzaneh, P., Afsaneh, M., Reza, R., and Masood, N., Source localization of the effects of Persian classical music forms on the brain waves by QEEG. Procedia Soc. Behav. Sci. 5(2):770–773, 2010. Lin, Y. P., Duann, J. R., Chen, J. H., and Jung, T. P., Electroencephalographic dynamics of musical emotion perception revealed by independent spectral components. Neuroreport 21(6):410–415, 2010. Adamos, D. A., Dimitriadis, S. I., and Laskaris, N. A., Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference. Inf. Sci. (Ny). 343–344:94–108, 2016. Lin, Y., Duann, J., Feng, W., Chen, J., and Jung, T., Revealing spatio-spectral electroencephalographic dynamics of musical mode and tempo perception by independent component analysis. J. Neuroeng. Rehabil. 11:1–11, 2014. Poikonen, H., Alluri, V., Brattico, E., Lartillot, O., Tervaniemi, M., and Huotilainen, M., Event-related brain responses while listening to entire pieces of music. Neuroscience 312:58–73, 2016. Rogenmoser, L., Zollinger, N., Elmer, S., and Jäncke, L., Independent component processes underlying emotions during natural music listening. Soc. Cogn. Affect. Neurosci. 11(9):1428–1439, 2016. F. Fikejz, Influence of compressed music bit rate on human electroencephalogram. pp. 1–4, 2012. Hassan, H., Murat, Z. H., Ross, V., and Buniyamin, N., A preliminary study on the effects of music on human brainwaves. 2012 Int. Conf. Control. Autom. Inf. Sci., pp. 176–180, 2012. Sanyal, S., et al., Detrended fluctuation and power spectral analysis of alpha and delta EEG brain rhythms to study music elicited emotion. Proc. 2015 Int. Conf. Signal Process. Comput. Control. ISPCC 2015, pp. 205–210, 2016. Flores-Gutiérrez, E. O. et al., Metabolic and electric brain patterns during pleasant and unpleasant emotions induced by music masterpieces. Int. J. Psychophysiol. 65(1):69–84, 2007. Wu, J., Zhang, J., Ding, X., Li, R., and Zhou, C., The effects of music on brain functional networks: A network analysis. Neuroscience 250:49–59, 2013. Lee, Y. Y., See, A. R., Chen, S. C., and Liang, C. K., Effect of Music Listening on Frontal EEG Asymmetry. Appl. Mech. Mater. 311:502–506, 2013. Mikutta, C., Altorfer, A., Strik, W., and Koenig, T., Emotions, arousal, and frontal alpha rhythm asymmetry during beethoven’s 5th symphony. Brain Topogr. 25(4):423–430, 2012. Maity, A. K., Pratihar, R., Agrawal, V., Mitra, A., and Dey, S., Multifractal detrended fluctuation analysis of the music induced EEG signals. pp. 252–257, 2015. Uma, M. and Sridhar, S. S., A feasibility study for developing an emotional control system through brain computer interface. 2013 Int. Conf. Hum. Comput. Interact., pp. 1–6, 2013. Hatamikia, S. and Nasrabadi, A. M., Recognition of emotional states induced by music videos based on nonlinear feature extraction and SOM classification. 2014 21st Iran. Conf. Biomed. Eng. ICBME 2014, no. Icbme, pp. 333–337, 2011. Zhang, Y., Ji, X., and Zhang, S., An approach to EEG-based emotion recognition using combined feature extraction method. Neurosci. Lett. 633:152–157, 2016. Uzun, S. S., Yildirim, S., and Yildirim, E., Emotion primitives estimation from EEG signals using Hilbert Huang Transform. Proc. - IEEE-EMBS Int. Conf. Biomed. Heal. Informatics Glob. Gd. Chall. Heal. Informatics, BHI 2012 1(Bhi):224–227, 2012. Ma, X., and Yang, F., An empirical study on interest point ranking and valence-arousal tags of EEG data. 2015 8th Int. Symp. Comput. Intell. Des., pp. 499–502, 2015. Ogawa, T., Ito, S., Mitsukura, Y., Fukumi, M., and Akamatsua, N., Feature extraction from eeg patterns in music listening. Ieee Ispacs 2004:17–21, 2004. Daly, I. et al., Music-induced emotions can be predicted from a combination of brain activity and acoustic features. Brain Cogn. 101:1–11, 2015. Nakamura, S., Sadato, N., Oohashi, T., Nishina, E., Fuwamoto, Y., and Yonekura, Y., Analysis of music-brain interaction with simultaneous measurement of regional cerebral blood flow and electroencephalogram beta rhythm in human subjects. Neurosci. Lett. 275(3):222–226, 1999. Banerjee, A. et al., Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals. Phys. A Stat. Mech. its Appl. 444:110–120, 2016. Lin, L. C. et al., The long-term effect of listening to Mozart K.448 decreases epileptiform discharges in children with epilepsy. Epilepsy Behav. 21(4):420–424, 2011. Lin, L. C. et al., Parasympathetic activation is involved in reducing epileptiform discharges when listening to Mozart music. Clin. Neurophysiol. 124(8):1528–1535, 2013. Hoseingholizade, S., Golpaygani, M. R. H., and Monfared, A. S., Studying emotion through nonlinear processing of EEG. Procedia Soc. Behav. Sci. 32(2010):163–169, 2012. Jäncke, L., and Alahmadi, N., Detection of independent functional networks during music listening using electroencephalogram and {sLORETA-ICA.}. Neuroreport 27(6):455–461, 2016. Marsella, P., Scorpecci, A., Vecchiato, G., Maglione, A. G., Colosimo, A., and Babiloni, F., Neuroelectrical imaging investigation of cortical activity during listening to music in prelingually deaf children with cochlear implants. Int. J. Pediatr. Otorhinolaryngol. 78(5):737–743, 2014. Verrusio, W., Ettorre, E., Vicenzini, E., Vanacore, N., Cacciafesta, M., and Mecarelli, O., The Mozart Effect: A quantitative EEG study. Conscious. Cogn. 35:150–155, 2015. Kwon, M., Gang, M., and Oh, K., Effect of the group music therapy on brain wave, behavior, and cognitive function among patients with chronic schizophrenia. Asian Nurs. Res. (Korean. Soc. Nurs. Sci). 7(4):168–174, 2013. Akdemir Akar, S., Kara, S., Agambayev, S., and Bilgiç, V., Nonlinear analysis of EEGs of patients with major depression during different emotional states. Comput. Biol. Med. 67:49–60, 2015. O’Kelly, J., James, L., Palaniappan, R., Taborin, J., Fachner, J., and Magee, W. L. L., Neurophysiological and behavioral responses to music therapy in vegetative and minimally conscious States. Front. Hum. Neurosci. 7:884, 2013. Erkkilä, J., Gold, C., Fachner, J., Ala-Ruona, E., Punkanen, M., and Vanhala, M., The effect of improvisational music therapy on the treatment of depression: protocol for a randomised controlled trial. BMC Psychiatry 8(1):50, 2008. Lense, M. D., Gordon, R. L., Key, A. P. F., and Dykens, E. M., Neural correlates of cross-modal affective priming by music in williams syndrome. Soc. Cogn. Affect. Neurosci. 9(4):529–537, 2014. Tan, L. F., Dienes, Z., Jansari, A., and Goh, S. Y., Effect of mindfulness meditation on brain-computer interface performance. Conscious. Cogn. 23(1):12–21, 2014. Jaušovec, N., Jaušovec, K., and Gerlič, I., The influence of Mozart’s music on brain activity in the process of learning. Clin. Neurophysiol. 117(12):2703–2714, 2006. L. L. Chen, B. Wang, and J. Z. Zoul, “Effect Evaluation of Relaxation Training Based on Nonlinear Parameters of,” pp. 2–5. Poikonen, H., Toiviainen, P., and Tervaniemi, M., Early auditory processing in musicians and dancers during a contemporary dance piece. Nat. Publ. Gr. 35:1–11, 2016. Rigoulot, S., Pell, M. D., and Armony, J. L., Time course of the influence of musical expertise on the processing of vocal and musical sounds. Neuroscience 290:175–184, 2015. Fikejz, F., Influence of music on human electroenc ephalogram. Appl. Electron., pp. 1–4, 2011. Baumgartner, T., Esslen, M., and Jäncke, L., From emotion perception to emotion experience: Emotions evoked by pictures and classical music. Int. J. Psychophysiol. 60(1):34–43, 2006. Ghazal, R. S. S. A., Kadir M. H., Murat, Z. H., Taib, M. N., Rahman, H. A., Aris, S. A. M., The preliminary study on the effect ofnasyid music and rock music on brainwave signal using EEG. Eng. Educ. (ICEED), 2010 2nd Int. Congr., pp. 58–63, 2010. Naraballobh, J., Thanapatay, D., Chinrungrueng, J., and Nishihara, A., Effect of auditory stimulus in EEG signal using a Brain-Computer Interface. ECTI-CON 2015–2015 12th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol., 2015. Naraballobh, J. and Thanapatay, D., EEG-based analysis of auditory stimulus in a brain-computer interface. 2015 6th Int. Conf. Inf. Commun. Technol. Embed. Syst. EEG-Based, pp. 6–9, 2015. Al-Galal, S. A. Y., Alshaikhli, I. F. T., Rahman, A. W. B. A., and Dzulkifli, M. A., EEG-based emotion recognition while listening to quran recitation compared with relaxing music using valence-arousal model. Proc. - 2015 4th Int. Conf. Adv. Comput. Sci. Appl. Technol. ACSAT 2015, pp. 245–250, 2016. Lin, Y. P., Jung, T. P., and Chen, J. H., EEG dynamics during music appreciation. Proc. 31st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Eng. Futur. Biomed. EMBC 2009, pp. 5316–5319, 2009. Ito, S. I., Mitsukura, Y., Fukumi, M., and Cao, J., Method for detecting music to match the user’s mood in prefrontal cortex electroencephalogram activity based on individual characteristics. Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., pp. 2640–2644, 2007. Unehara, M., Yamada, K., and Shimada, T., Subjective evaluation of music with brain wave analysis for interactive music composition by IEC. 2014 Jt. 7th Int. Conf. Soft Comput. Intell. Syst. 15th Int. Symp. Adv. Intell. Syst., pp. 66–70, 2014. Li, Q., Yang, Z., Liu, S., Dai, Z., and Liu, Y., The Study of Emotion Recognition from Physiological Signals. Seventh Int. Conf. Adv. Comput. Intell., pp. 378–382, 2015. Jia-wei, S. and Wen, C. S., A study on non-invasive brainwave optimization. Kroupi, E., Vesin, J. M., and Ebrahimi, T., Phase-amplitude coupling between EEG and EDA while experiencing multimedia content,” Proc. - 2013 Hum. Assoc. Conf. Affect. Comput. Intell. Interact. ACII 2013, pp. 865–870, 2013. Sammler, D., Grigutsch, M., Fritz, T., and Koelsch, S., Music and emotion: Electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology 44(2):293–304, 2007.