Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results


  • José Humberto Trueba Perdomo Instituto Tecnológico de Orizaba
  • Ignacio Herrera Aguilar Electronics Department, Mexico National Institute of Technology/Orizaba Institute of Technology. Oriente 9, 94320 Orizaba, Mexico
  • Francesca Gasparini Department of Informatics, Systems, and Communication, University of Milano Bicocca. Viale Sarca 336, 20126 Milano, Italy.


Palabras clave:

Application, Wavelet, Electroencephalogram, Signal, Analysis


This paper presents a new application for analyzing electroencephalogram (EEG) signals. The signals are pre-filtered through MATLAB's EEGLAB tool. The created application performs a convolution between the original EEG signal and a complex Morlet wavelet. As a final result, the application shows the signal power value and a spectrogram of the convoluted signal. Moreover, the created application compares different EEG channels at the same time, in a fast and straightforward way, through a time and frequency analysis. Finally, the effectiveness of the created application was demonstrated by performing an analysis of the alpha signals of healthy subjects, one signal created by the subject with eyes closed and the other, with which it was compared, was created by the same subject with eyes open. This also served to demonstrate that the power of the alpha band of the closed-eyed signal is higher than the power of the open-eyed signal.


Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123(1), 69–87.

Bhattacharyya, A., Pachori, R. B., & Acharya, U. R. (2017). Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy, 19(3), 99.

Butler, S. R., & Glass, A. (1974). Asymmetries in the electroencephalogram associated with cerebral dominance. Electroencephalography and Clinical Neurophysiology, 36, 481–491.

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, Pc., Mark, R., … Stanley, H. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23), 215–220.

Helfman, J. (2015). Hue tinting for interactive data visualization. Human Vision and Electronic Imaging XX. Society of Photo-Optical Instrumentation Engineers (SPIE), 9394, 1–12.

Kan, D. P. X., Croarkin, P. E., Phang, C. K., & Lee, P. F. (2017). EEG differences between eyes-closed and eyes-open conditions at the resting stage for euthymic participants. Neurophysiology, 49(6), 432–440.

Mann, C. A., Lubar, J. F., Zimmerman, A. W., Miller, C. A., & Muenchen, R. A. (1991). Quantitative analysis of EEG in boys with attention-deficit-hyperactivity disorder : Controlled study with clinical implications. Pediatric Neurology, 8(1), 30–36.

Morgan, A. H., Mcdonald, P. J., & Macdonald, H. (1971). Differences in bilateral alpha activity as a function of experimental task, with a note on lateral eye movements and hypnotizability. Neuropsychologia, 9(4), 459–469.

Omidvarnia, A., Pedersen, M., Vaughan, D. N., Walz, J. M., Abbott, D. F., Zalesky, A., & Jackson, G. D. (2017). Dynamic coupling between fMRI local connectivity and interictal EEG in focal epilepsy : A wavelet analysis approach. Human Brain Mapping, 38(11), 5356–5374.

Pattnaik, S., Dash, M., & Sabut, S. K. (2016). DWT-based feature extraction and classification for motor imaginary EEG signals. Proceedings of 2016 International Conference on Systems in Medicine and Biology, (January), 186–191.

Penttilä, M., Partanen, J. V., Soininen, H., & Riekkinen, P. J. (1985). Quantitative analysis of occipital EEG in different stages of Alzheimer’s disease. Electroencephalography and Clinical Neurophysiology, 60(1), 1–6.

Schalk, G., Mcfarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004). BCI2000 : A General-Purpose Brain-Computer Interface ( BCI ) System. IEEE Transactions on Biomedical Engineering, 51(6), 1034–1043.

Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1–11.

Valipour, S., Shaligram, A. D., & Kulkarni, G. R. (2013). Spectral analysis of EEG signal for detection of alpha rhythm with open and closed eyes. International Journal of Engineering and Innovative Technology (IJEIT), 3(6), 1–4.

X Cohen, M. (2014). Analyzing neural time series data: Theory and practice. London, UK: The MIT Press.

Zhang, Y., Liu, B., Ji, X., & Huang, D. (2016). Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Process Lett, 45(2), 365–378.




Cómo citar

Trueba Perdomo, J. H., Herrera Aguilar, I., & Gasparini, F. (2020). Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results. ReCIBE, Revista electrónica De Computación, Informática, Biomédica Y Electrónica, 8(2), B–1.