Coherence analysis of EEG in locomotion using graphs


  • G. Quiroz Universidad Autónoma de Nuevo León, FIME,
  • A. Espinoza Valdez Departamento de Ciencias Computacionales, CUCEI
  • R. A. Salido Ruiz Departamento de Ciencias Computacionales, CUCEI
  • L. Mercado Universidad Autónoma de Nuevo León, FIME



Coherence, feature extraction, graphs, EEG processing.


One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.


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How to Cite

Quiroz, G., Espinoza Valdez, A., Salido Ruiz, R. A., & Mercado, L. (2017). Coherence analysis of EEG in locomotion using graphs. Revista Mexicana De Ingenieria Biomedica, 38(1), 235–246.



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