Research on the Effect of Background Music on Working Memory Based on Granger Causal Network

[1] Felix, U. (1993). The contribution of background music to the enhancement of learning in suggestopedia: A critical review of the literature. Journal of the Society for Accelerative Learning and Teaching, 18, 277–282. Felix U. ( 1993 ). The contribution of background music to the enhancement of learning in suggestopedia: A critical review of the literature . Journal of the Society for Accelerative Learning and Teaching , 18 , 277 – 282 . Search in Google Scholar

[2] Rauscher, F. H., Shaw, G. L., & Ky, K. N. (1995). Listening to Mozart enhances spatial-temporal reasoning: Towards a neurophysiological basis. Neuroscience Letters, 185(1), 44–47. Rauscher F. H. Shaw G. L. Ky K. N. ( 1995 ). Listening to Mozart enhances spatial-temporal reasoning: Towards a neurophysiological basis . Neuroscience Letters , 185 ( 1 ), 44 – 47 . Search in Google Scholar

[3] Anderson, S., Henke, J., & McLaughlin, M., et al. (2000). Using background music to enhance memory and improve learning. Action Research, 48. Anderson S. Henke J. McLaughlin M. ( 2000 ). Using background music to enhance memory and improve learning . Action Research , 48 . Search in Google Scholar

[4] Goldstein, A., Sandhans. (1998). The role of background music on auditory memory in children with language difficulties. International Journal of Psychophysiology, 51(3), 389–401. Goldstein A. Sandhans . ( 1998 ). The role of background music on auditory memory in children with language difficulties . International Journal of Psychophysiology , 51 ( 3 ), 389 – 401 . Search in Google Scholar

[5] George, E. M., & Coch, D. (2011). Music training and working memory: An ERP study. Neuropsychologia, 49(5), 1083–1094. George E. M. Coch D. ( 2011 ). Music training and working memory: An ERP study . Neuropsychologia , 49 ( 5 ), 1083 – 1094 . Search in Google Scholar

[6] Micheloyannis, S. (2006). Small-world networks and disturbed functional connectivity in schizophrenia. Schizophrenia Research, 87(1), 60–66. Micheloyannis S. ( 2006 ). Small-world networks and disturbed functional connectivity in schizophrenia . Schizophrenia Research , 87 ( 1 ), 60 – 66 . Search in Google Scholar

[7] Stam, C., Jones, B., & Nolte, G., et al. (2006). Small-world networks and functional connectivity in Alzheimer’s disease. Cerebral Cortex, 17(1), 92–99. Stam C. Jones B. Nolte G. ( 2006 ). Small-world networks and functional connectivity in Alzheimer’s disease . Cerebral Cortex , 17 ( 1 ), 92 – 99 . Search in Google Scholar

[8] Haan, W. D., Pijnenburg, Y. A., & Strijers, R. L., et al. (2009). Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory. BMC Neuroscience, 10(1), 101. Haan W. D. Pijnenburg Y. A. Strijers R. L. ( 2009 ). Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory . BMC Neuroscience , 10 ( 1 ), 101 . Search in Google Scholar

[9] Wiener, N. (1956). The theory of prediction. Modern Mathematics for the Engineer. Wiener N. ( 1956 ). The theory of prediction . Modern Mathematics for the Engineer . Search in Google Scholar

[10] Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(37), 424–438. Granger C. W. J. ( 1969 ). Investigating causal relations by econometric models and cross-spectral methods . Econometrica , 37 ( 37 ), 424 – 438 . Search in Google Scholar

[11] Palaniyappan, L., Simmonite, M., White, T., et al. (2013). Neural primacy of the salience processing system in schizophrenia. Neuron, 79(4), 814–828. Palaniyappan L. Simmonite M. White T. ( 2013 ). Neural primacy of the salience processing system in schizophrenia . Neuron , 79 ( 4 ), 814 – 828 . Search in Google Scholar

[12] de la Iglesia-Vaya, M., Maria, J. E., & Jose, M. M., et al. (2014). Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations. NeuroImage: Clinical, 6, 171–179. de la Iglesia-Vaya M. Maria J. E. Jose M. M. ( 2014 ). Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations . NeuroImage: Clinical , 6 , 171 – 179 . Search in Google Scholar

[13] Qin, Y., Xu, P., & Yao, D. (2010). A comparative study of different references for EEG default mode network: The use of the infinity reference. Clinical Neurophysiology, 121(12), 1981–1991. Qin Y. Xu P. Yao D. ( 2010 ). A comparative study of different references for EEG default mode network: The use of the infinity reference . Clinical Neurophysiology , 121 ( 12 ), 1981 – 1991 . Search in Google Scholar

[14] Yao, D. (2001). A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiological Measurement, 22(4), 693–711. Yao D. ( 2001 ). A method to standardize a reference of scalp EEG recordings to a point at infinity . Physiological Measurement , 22 ( 4 ), 693 – 711 . Search in Google Scholar

[15] Dong, L., Li, F., & Liu, Q., et al. (2017). MATLAB toolboxes for reference electrode standardization technique (REST) of scalp EEG. Frontiers in Neuroscience, 11, 601. Dong L. Li F. Liu Q. ( 2017 ). MATLAB toolboxes for reference electrode standardization technique (REST) of scalp EEG . Frontiers in Neuroscience , 11 , 601 . Search in Google Scholar

[16] Ramnani, N., Penny, W., & Matthews, P. (2004). New approaches for exploring anatomical and functional connectivity in the human brain. Biological Psychiatry, 56(9), 613–619. Ramnani N. Penny W. Matthews P. ( 2004 ). New approaches for exploring anatomical and functional connectivity in the human brain . Biological Psychiatry , 56 ( 9 ), 613 – 619 . Search in Google Scholar

[17] McIntosh, A. R., & Gonzalez-Lima, F. (1994). Structural equation modeling and its application to network analysis in functional brain imaging. Human Brain Mapping, 2(1-2), 2–22. McIntosh A. R. Gonzalez-Lima F. ( 1994 ). Structural equation modeling and its application to network analysis in functional brain imaging . Human Brain Mapping , 2 ( 1-2 ), 2 – 22 . Search in Google Scholar

[18] Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21(1), 243–247. Akaike H. ( 1969 ). Fitting autoregressive models for prediction . Annals of the Institute of Statistical Mathematics , 21 ( 1 ), 243 – 247 . Search in Google Scholar

[19] Calhoun, V. D., Eichele, T., & Tülay, A., et al. (2012). Decomposing the brain: Components and modes, networks and nodes. Trends in Cognitive Sciences, 16(5), 256. Calhoun V. D. Eichele T. Tülay A. ( 2012 ). Decomposing the brain: Components and modes, networks and nodes . Trends in Cognitive Sciences , 16 ( 5 ), 256 . Search in Google Scholar

[20] Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069. Rubinov M. Sporns O. ( 2010 ). Complex network measures of brain connectivity: Uses and interpretations . Neuroimage , 52 ( 3 ), 1059 – 1069 . Search in Google Scholar

[21] Protopapa, F., Siettos, C. I., & Evdokimidis, I., et al. (2014). Granger causality analysis reveals distinct spatio-temporal connectivity patterns in motor and perceptual visuo-spatial working memory. Frontiers in Computational Neuroscience, 8(8), 146. Protopapa F. Siettos C. I. Evdokimidis I. ( 2014 ). Granger causality analysis reveals distinct spatio-temporal connectivity patterns in motor and perceptual visuo-spatial working memory . Frontiers in Computational Neuroscience , 8 ( 8 ), 146 . Search in Google Scholar

[22] Miao, X., Xia, W., & Rui, L., et al. (2011). Altered connectivity pattern of hubs in default-mode network with Alzheimer’s disease: A Granger causality modeling approach. PLoS One, 6(10), 60–66. Miao X. Xia W. Rui L. ( 2011 ). Altered connectivity pattern of hubs in default-mode network with Alzheimer’s disease: A Granger causality modeling approach . PLoS One , 6 ( 10 ), 60 – 66 . Search in Google Scholar

[23] Lehrer, J. (2009). Neuroscience: Making connections. Nature, 457, 524–527. Lehrer J. ( 2009 ). Neuroscience: Making connections . Nature , 457 , 524 – 527 . Search in Google Scholar

[24] Yin, Z. L., Li, J., & Zhang, Y., et al. (2017). Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series. Biomedical Signal Processing and Control, 31, 331–338. Yin Z. L. Li J. Zhang Y. ( 2017 ). Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series . Biomedical Signal Processing and Control , 31 , 331 – 338 . Search in Google Scholar