LEDA-Localized-EEG Dynamics Analyzer: a MATLAB-Based Innovative Toolbox for Analysis of EEG Source Dynamics


Establishing conclusive cortical activity dynamics from neuroimages and high dimensional neuronal data post-processing, such as scalp-EEG/ERP or its localized source data, is always challenging. For addressing this, we introduce LEDA, localized-EEG dynamics analyzer, offering our novel techniques, namely (1)the localized source activity to duration (LSAD) ratio that elegantly combines voxel activation with temporal information enabling a spatiotemporal condition comparison, (2)cortical region’s activation-duration summary generation, (3)anatomical dominance calculator, and (4)voxel activations’ GIF animation extraction feature. They effectively explore and quantify physiological spatiotemporal dynamics and provide insights more intuitive than EEG/ERP biomarkers. To demonstrate, we chose to study sensory gating deficits in schizophrenia for which the P50 wave biomarker is widely discussed. EEG response (64 channels@1 kHz) to auditory paired-click paradigm from 9 patients and 9 controls was processed to ERP. Source localization was achieved by using sLORETA, a widely-used source reconstruction algorithm. LSAD ratio of patients significantly differed from that of controls for the auditory cortical regions (p value < 0.05). It revealed that inhibitory alpha activity pattern was prominent in parietal, temporal and occipital lobe for controls and not patients. Conversely, the frontal lobe, specifically the middle frontal gyrus, was temporally the most active analogously across all subjects. LEDA also extracted that swift alpha activity exists between 100 and 300 ms time period. These derived results match with existing fMRI studies, speak beyond P50 wave suppression biomarker and emphasize that our methods successfully uncovered meaningful brain mechanisms at high spatiotemporal resolution. They are provided as an open-source MATLAB based toolbox for applicability to other neuronal pathological investigations.

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  1. 1.

    Haas, L. F. (2003). Hans berger (1873–1941), richard caton (1842–1926), and electroencephalography. Journal of Neurology, Neurosurgery & Psychiatry, 74(1), 9–9.

    Article  Google Scholar 

  2. 2.

    Delorme, A., Kothe, C., Vankov, A., Bigdely-Shamlo, N., Oostenveld, R., Zander, T. O., & Makeig, S. (2010). MATLAB-based tools for BCI research. In Brain-computer interfaces (pp. 241-259). Springer, London.

  3. 3.

    Sur, S., & Sinha, V. K. (2009). Event-related potential: An overview. Industrial Psychiatry Journal, 18(1), 70–73. https://doi.org/10.4103/0972-6748.57865.

    Article  Google Scholar 

  4. 4.

    Meyer-Lindenberg, A. (2010). From maps to mechanisms through neuroimaging of schizophrenia. Nature, 468(7321) Art. no. 7321, 194–202. https://doi.org/10.1038/nature09569.

    Article  Google Scholar 

  5. 5.

    Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol, 24(Suppl D), 5–12.

    Google Scholar 

  6. 6.

    Zariffa, J., & Popovic, M. R. (Feb. 2009). Localization of active pathways in peripheral nerves: A simulation study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(1), 53–62. https://doi.org/10.1109/TNSRE.2008.2010475.

    Article  Google Scholar 

  7. 7.

    Cao, C., & Slobounov, S. (Feb. 2010). Alteration of cortical functional connectivity as a result of traumatic brain injury revealed by graph theory, ICA, and sLORETA analyses of EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(1), 11–19. https://doi.org/10.1109/TNSRE.2009.2027704.

    Article  Google Scholar 

  8. 8.

    Zhao, Q., Jiang, H., Hu, B., Li, Y., Zhong, N., Li, M., Lin, W., & Liu, Q. (Jul. 2017). Nonlinear dynamic complexity and sources of resting-state EEG in abstinent heroin addicts. IEEE Transactions on Nanobioscience, 16(5), 349–355. https://doi.org/10.1109/TNB.2017.2705689.

    Article  Google Scholar 

  9. 9.

    Cosandier-Rimélé, D., Ramantani, G., Zentner, J., Schulze-Bonhage, A., & Dümpelmann, M. (2017). A realistic multimodal modeling approach for the evaluation of distributed source analysis: application to sLORETA. J. Neural Eng., 14(5), 056008. https://doi.org/10.1088/1741-2552/aa7db1.

    Article  Google Scholar 

  10. 10.

    Iwaki, T., Hayashi, M., & Hori, T. (1997). Changes in alpha band Eeg activity in the frontal area after stimulation with music of different affective content changes in alpha band Eeg activity in the frontal area after stimulation with music of different affective content. Perceptual and Motor Skills, 84(2), 515–526. https://doi.org/10.2466/pms.1997.84.2.515.

    Article  Google Scholar 

  11. 11.

    Yordanova, J. Y., Kolev, V. N., & Başar, E. (1998). EEG theta and frontal alpha oscillations during auditory processing change with aging. Clinical Neurophysiology, 108(5), 497–505. https://doi.org/10.1016/S0168-5597(98)00028-8.

    Article  Google Scholar 

  12. 12.

    Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition–timing hypothesis. Brain Research Reviews, 53(1), 63–88. https://doi.org/10.1016/j.brainresrev.2006.06.003.

    Article  Google Scholar 

  13. 13.

    Kathmann, N., & Engel, R. R. (1990). Sensory gating in normals and schizophrenics: A failure to find strong P50 suppression in normals. Biological Psychiatry, 27(11), 1216–1226.

    Article  Google Scholar 

  14. 14.

    Mayer, A. R., et al. (2009). The neural networks underlying auditory sensory gating. Neuroimage, 44(1), 182–189. https://doi.org/10.1016/j.neuroimage.2008.08.025.

    Article  Google Scholar 

  15. 15.

    Tregellas, J. R., Davalos, D. B., Rojas, D. C., Waldo, M. C., Gibson, L., Wylie, K., du, Y. P., & Freedman, R. (2007). Increased hemodynamic response in the Hippocampus, thalamus and prefrontal cortex during abnormal sensory gating in schizophrenia. Schizophrenia Research, 92(1–3), 262–272. https://doi.org/10.1016/j.schres.2006.12.033.

    Article  Google Scholar 

  16. 16.

    Campbell, L. E., Hughes, M., Budd, T. W., Cooper, G., Fulham, W. R., Karayanidis, F., Hanlon, M. C., Stojanov, W., Johnston, P., Case, V., & Schall, U. (2007). Primary and secondary neural networks of auditory prepulse inhibition: A functional magnetic resonance imaging study of sensorimotor gating of the human acoustic startle response. European Journal of Neuroscience, 26(8), 2327–2333.

    Article  Google Scholar 

  17. 17.

    Bak, N., Glenthoj, B. Y., Rostrup, E., Larsson, H. B., & Oranje, B. (2011). Source localization of sensory gating: A combined EEG and fMRI study in healthy volunteers. NeuroImage, 54(4), 2711–2718. https://doi.org/10.1016/j.neuroimage.2010.11.039.

    Article  Google Scholar 

  18. 18.

    Freedman, R., Adler, L. E., Gerhardt, G. A., Waldo, M., Baker, N., Rose, G. M., Drebing, C., Nagamoto, H., Bickford-Wimer, P., & Franks, R. (1987). Neurobiological studies of sensory gating in schizophrenia. Schizophrenia Bulletin, 13(4), 669–678. https://doi.org/10.1093/schbul/13.4.669.

    Article  Google Scholar 

  19. 19.

    Adler, L. E., Olincy, A., Cawthra, E. M., McRae, K. A., Harris, J. G., Nagamoto, H. T., Waldo, M. C., Hall, M. H., Bowles, A., Woodward, L., Ross, R. G., & Freedman, R. (2004). Varied effects of atypical neuroleptics on P50 auditory gating in schizophrenia patients. The American Journal of Psychiatry, 161(10), 1822–1828. https://doi.org/10.1176/ajp.161.10.1822.

    Article  Google Scholar 

  20. 20.

    Potter, D., Summerfelt, A., Gold, J., & Buchanan, R. W. (2006). Review of clinical correlates of P50 sensory gating abnormalities in patients with schizophrenia. Schizophrenia Bulletin, 32(4), 692–700. https://doi.org/10.1093/schbul/sbj050.

    Article  Google Scholar 

  21. 21.

    Knott, V., Millar, A., & Fisher, D. (2009). Sensory gating and source analysis of the auditory P50 in low and high suppressors. Neuroimage, 44(3), 992–1000.

    Article  Google Scholar 

  22. 22.

    Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: An open-source toolbox for the analysis of event-related potentials. Frontiers in Human Neuroscience, 8, 213.

    Article  Google Scholar 

  23. 23.

    Talairach, J. (1988). 3-dimensional proportional system; an approach to cerebral imaging. co-planar stereotaxic atlas of the human brain. Thieme, 1–122.

  24. 24.

    Thoma, R. J., Hanlon, F. M., Moses, S. N., Edgar, J. C., Huang, M., Weisend, M. P., Irwin, J., Sherwood, A., Paulson, K., Bustillo, J., Adler, L. E., Miller, G. A., & Cañive, J. M. (Sep. 2003). Lateralization of auditory sensory gating and neuropsychological dysfunction in schizophrenia. AJP, 160(9), 1595–1605. https://doi.org/10.1176/appi.ajp.160.9.1595.

    Article  Google Scholar 

  25. 25.

    Krol, L. R., Pawlitzki, J., Lotte, F., Gramann, K., & Zander, T. O. (2018). SEREEGA: Simulating event-related EEG activity. Journal of Neuroscience Methods, 309, 13–24.

    Article  Google Scholar 

  26. 26.

    Uchida, N., Kepecs, A., & Mainen, Z. F. (2006). Seeing at a glance, smelling in a whiff: Rapid forms of perceptual decision making. Nature Reviews Neuroscience, 7(6), 485–491.

    Article  Google Scholar 

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This study was supported by NSF grant ECCS-1631820, NIH grants MH112180, MH108148, MH103222, and a Brain and Behavior Research Foundation grant. We thank Dr. Jyotsna Aggarwal, Dr. Surajit Bhattacharya, Mr. Alex Miu and Amrithya Balasubramanian for their efforts towards contribution of our manuscript.

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Correspondence to Deepa Gupta.

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Supplementary Information

Excel file containing a sample subject’s dataset for the first auditory click to show how ratio and duration statistics were computed.

GIF file animation of a sample subject dataset

Toolbox is available at https://gitlab.com/deepagupta/LEDA


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Gupta, D., Summerfelt, A., Luzhansky, J. et al. LEDA-Localized-EEG Dynamics Analyzer: a MATLAB-Based Innovative Toolbox for Analysis of EEG Source Dynamics. J Sign Process Syst (2021). https://doi.org/10.1007/s11265-020-01617-z

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  • EEG
  • Source localization
  • Schizophrenia
  • Auditory paired click paradigm
  • Sensory gating
  • Alpha wave