Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

Mario Ortiz, Eduardo Iáñez, José L Contreras-Vidal, José M Azorín, Mario Ortiz, Eduardo Iáñez, José L Contreras-Vidal, José M Azorín

Abstract

The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showcasing it as a viable tool for future BMI algorithms based on motor related events.

Keywords: brain-machine interface; electroencephalography; empirical mode decomposition; exoskeleton; frequency analysis; motor imagery.

Copyright © 2020 Ortiz, Iáñez, Contreras-Vidal and Azorín.

Figures

Figure 1
Figure 1
Image of a subject executing one of the experimental trials.
Figure 2
Figure 2
Example of trial: (A) Details of the activation command. (B) Details of the MI tasks ~20 s (red) and relaxed state event ~10 + 10 s (blue), and moving reverse count (black top).
Figure 3
Figure 3
Empirical mode decomposition of one of the trials of subject S2 for Cz electrode. MI periods appear in red and relaxed state periods in blue. Each of the modes corresponds to the local oscillations of the signal in descendent value of frequency.
Figure 4
Figure 4
Instantaneous frequency of the first 5 IMFs obtained by EMD of one of the trials of subject S2. Each IMF oscillates within the EEG rhythms: IMF1 (Gamma), IMF2 (Beta), IMF3 (Alpha), IMF4 (Theta), and IMF5 (Delta).
Figure 5
Figure 5
Boxplot of the variation of power of the MI tasks related to the relaxed state: (A) Left image shows the variation of power for the nine electrodes averaged through the 10 trials placed in the motor cortex zone. (B) Left one shows it for the electrode Cz for the individual 10 single trials.
Figure 6
Figure 6
Instantaneous power of IMF3 after the acoustic cue for MI (black line) for trial 1 of subject S1. The value is compared with the instantaneous power of the signal filtered by a Butterworth filter (5–15 Hz). The peak response is higher using the IMF. REX start of movement is represented by a red spot.
Figure 7
Figure 7
Evolution of the instantaneous power for trial 1 of subject S2: (A) IMF1. (B) IMF2.

References

    1. Amzica F., Steriade M. (1998). Electrophysiological correlates of sleep delta waves. Electroencephalogr. Clin. Neurophysiol. 107, 69–83. 10.1016/S0013-4694(98)00051-0
    1. Andy F. (2013). Discovering Statistics Using SPSS Statistics, 4th Edn. Los Angeles, CA: Sage Publications.
    1. Costa A., Iáñez E., Úbeda A., Hortal E., Del-Ama A. J., Gil-Agudo A., et al. . (2016). Decoding the attentional demands of gait through EEG gamma band features. PLoS ONE 11:e0154136. 10.1371/journal.pone.0154136
    1. Costa-García A., Iáñez E., Del-Ama A., Gil-Águdo A., Azorín J. (2019). EEG model stability and online decoding of attentional demand during gait using gamma band features. Neurocomputing 360, 151–162. 10.1016/j.neucom.2019.06.021
    1. Del Castillo M. D., Serrano J. I., Rocon E., Lerma S., Martínez I. (2018). Neurophysiologic assessment of motor imagery training by using virtual reality for pediatric population with cerebral palsy. Rev. Iberoam. Autom. Inform. Ind. 15, 174–179. 10.4995/riai.2017.8819
    1. Ghasemi A., Zahediasl S. (2012). Normality tests for statistical analysis: a guide for non-statisticians. Int. J. Endocrinol. Metab. 10, 486–489. 10.5812/ijem.3505
    1. Gui K., Liu H., Zhang D. (2017). Toward multimodal human-robot interaction to enhance active participation of users in gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 2054–2066. 10.1109/TNSRE.2017.2703586
    1. He Y., Eguren D., Azorín J. M., Grossman R. G., Luu T. P., Contreras-Vidal J. L. (2018). Brain-machine interfaces for controlling lower-limb powered robotic systems. J. Neural Eng. 15:021004. 10.1088/1741-2552/aaa8c0
    1. Huang N. E., Shen Z., Long S. R., Wu M. C., Shih H. H., Zheng Q., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci. 454, 903–995. 10.1098/rspa.1998.0193
    1. Jeon Y., Nam C. S., Kim Y. J., Whang M. C. (2011). Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: implications for brain-computer interfaces. Int. J. Ind. Ergonom. 41, 428–436. 10.1016/j.ergon.2011.03.005
    1. Kant P., Hazarika J., Laskar S. H. (2019). Wavelet transform based approach for EEG feature selection of motor imagery data for braincomputer interfaces, in Proceedings of the 3rd International Conference on Inventive Systems and Control, ICISC 2019 (Coimbatore: Institute of Electrical and Electronics Engineers Inc.), 101–105. 10.1109/ICISC44355.2019.9036445
    1. Kilicarslan A., Grossman R. G., Contreras-Vidal J. L. (2016). A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements. J. Neural Eng. 13:026013. 10.1088/1741-2560/13/2/026013
    1. Kirmizi-Alsan E., Bayraktaroglu Z., Gurvit H., Keskin Y. H., Emre M., Demiralp T. (2006). Comparative analysis of event-related potentials during Go/NoGo and CPT: decomposition of electrophysiological markers of response inhibition and sustained attention. Brain Res. 1104, 114–128. 10.1016/j.brainres.2006.03.010
    1. Kwak N.-S., Müller K.-R., Lee S.-W. (2015). A lower limb exoskeleton control system based on steady state visual evoked potentials. J. Neural Eng. 12:056009. 10.1088/1741-2560/12/5/056009
    1. Li S., Zhou W., Yuan Q., Geng S., Cai D. (2013). Feature extraction and recognition of ictal EEG using EMD and SVM. Comput. Biol. Med. 43, 807–816. 10.1016/j.compbiomed.2013.04.002
    1. Looney D., Li L., Rutkowski T. M., Mandic D. P., Cichocki A. (2008). Ocular artifacts removal from EEG using EMD, in Advances in Cognitive Neurodynamics ICCN 2007 (Springer Netherlands: ), 831–835. 10.1007/978-1-4020-8387-7_145
    1. Martis R. J., Acharya U. R., Tan J. H., Petznick A., Yanti R., Chua C. K., et al. . (2012). Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals. Int. J. Neural Syst. 22:1250027. 10.1142/S012906571250027X
    1. Ortiz M., Rodríguez-Ugarte M., Iáñez E., Azorín J. M. (2017). Application of the Stockwell transform to electroencephalographic signal analysis during gait cycle. Front. Neurosci. 11:660. 10.3389/fnins.2017.00660
    1. Pfurtscheller G., Brunner C., Schlögl A., Lopes da Silva F. H. (2006). Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31, 153–159. 10.1016/j.neuroimage.2005.12.003
    1. Pfurtscheller G., Neuper C. (1994). Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neurosci. Lett. 174, 93–96. 10.1016/0304-3940(94)90127-9
    1. Pfurtscheller G., Neuper C., Flotzinger D., Pregenzer M. (1997). EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr. Clin. Neurophysiol. 103, 642–651. 10.1016/S0013-4694(97)00080-1
    1. Rao R. P. N. (2013). Brain-Computer Interfacing: An Introduction. Cambridge: Cambridge University Press; 10.1017/CBO9781139032803
    1. Rilling G., Flandrin P. (2008). One or two frequencies? The empirical mode decomposition answers. IEEE Trans. Signal Process. 56, 85–95. 10.1109/TSP.2007.906771
    1. Rutkowski T. M., Mandic D. P., Cichocki A., Przybyszewski A. W. (2010). EMD approach to multichannel EEG data the amplitude and phase components clustering analysis. J. Circuits Syst. Comput. 19, 215–229. 10.1142/S0218126610006037
    1. Seeber M., Scherer R., Wagner J., Solis-Escalante T., Müller-Putz G. R. (2014). EEG beta suppression and low gamma modulation are different elements of human upright walking. Front. Hum. Neurosci. 8:485. 10.3389/fnhum.2014.00485
    1. Shibasaki H., Hallett M. (2006). What is the Bereitschaftspotential? Clin. Neurophysiol. 117, 2341–2356. 10.1016/j.clinph.2006.04.025
    1. Xu B., Song A. (2008). Pattern recognition of motor imagery EEG using wavelet transform. J. Biomed. Sci. Eng. 01, 64–67. 10.4236/jbise.2008.11010
    1. Yang R., Song A., Xu B. (2010). Feature extraction of motor imagery EEG based on wavelet transform and higher-order statistics. Int. J. Wavelets Multires. Inform. Process. 8, 373–384. 10.1142/S0219691310003535
    1. Zhang X., Xu G., Member I., Xie J., Member I., Li M., et al. . (2015). An EEG-driven lower limb rehabilitation training system for active and passive co-stimulation, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Milan: EMBC; ), 4582–4585. 10.1109/EMBC.2015.7319414

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