Abstract
Motor imagery refers to the psychological realization of movements without movement or muscle activity; it is a research hotspot in neurophysiology, neuroimaging, neurology, and psychology; and it is used as a neurological rehabilitation of brain-computer interface technology. The foundation is widely studied. The EEG signal has a millisecond time resolution, which can facilitate the acquisition of neural signal changes in the body movement process. This article has selected the finger and thumb index finger movements as the task of motion imaging. Under the guidance of image and video, the characteristics of the brain electrical shock patterns during the finger-to-finger movement, thumb and forefinger pairing and unfolding were studied. The brain electrical signals of 15 healthy subjects were collected during the experiment, and the signals were analyzed for source location and information flow network. The result of source location analysis indicated that the activation of brain regions was mainly in the contra lateral SMA region, PMC region, and M1 region. However, the subjects were right-handed, so the characteristics of contra lateral activation were not obvious in the right-handed motor imaging process. Imagine that the opposite side of the pinching process is more powerful than the imaginary finger and the activation range is wider. The results of dynamic information flow of EEG signals at different stages of motor imaging show obvious contra lateral activation characteristics. The DTF analysis of the left-hand motion imaging finger pair pinching and unfolding shows that the information flow mainly flows from the right side of the brain to the left in the imagination process, while the right-hand motion imaging process is opposite, and the information flow mainly flows from the left side of the brain to the right side. The left hand imagines that the finger-to-pinch process is simpler and the number of connections is smaller than the information flow of the imagined finger deployment process. The right hand imagines that the finger-to-kneading process looks more complex than the imagined finger-expanding process, and the number of connections is more. However, its more connections appear mainly in the occipital region of the visual stimulus, the information flow in the frontal and parietal lobes is simple, and the number of connections is reduced. This article explores the relationship among the different movement phases of hand motion imaging and the change of EEG signals. The results show that the brain oscillation modes are similar and different, and the finger deployment process is used as a follow-up of the finger-to-kneading process. The process of knowing should be relatively simple.
Similar content being viewed by others
Change history
20 September 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s11042-022-13972-z
References
Bai X, Towle VL, He EJ et al (2007) Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI [J]. Neuroimage 35(2):598–608
Bartels A, Zeki S (2004) Functional brain mapping during free viewing of natural scenes [J]. Hum Brain Mapp 21(2):75–85
Bassett DS, Bullmore E (2006) Small-world brain networks [J]. Neuroscientist A Review Journal Bringing Neurobiology Neurology & Psychiatry 12(6):512–523
Cunnington R, Iansek R, Bradshaw JL et al (1996) Movement-related potentials associated with movement preparation and motor imagery [J]. Exp Brain Res 111(3):429–436
De VFF, Astolfi L, Cincotti F et al (2007) Cortical functional connectivity networks in normal and spinal cord injured patients: evaluation by graph analysis [J]. Hum Brain Mapp 28(12):1334–1346
do Nascimento OF, Farina D (2008) Movement-related cortical potentials allow discrimination of rate of torque development in imaginary isometric plantar flexion [J]. IEEE Trans Biomed Eng 55(11):2675–2678
Farina D, Nascimento OFD, Lucas MF et al (2007) Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters [J]. J Neurosci Methods 162(1–2):357–363
Gu Y, Farina D, Ramos MA et al (2009) Offline identification of imagined speed of wrist movements in paralyzed ALS patients from single-trial EEG [J]. Front Neurosci 3(3):62
Gu Y, Dremstrup K, Farina D (2009) Single-trial discrimination of type and speed of wrist movements from EEG recordings [J]. Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology 120(8):1596–1600
Gu Y, do Nascimento OF, Lucas MF et al (2009) Identification of task parameters from movement-related, cortical potentials [J]. Medical & Biological Engineering & Computing 47(12):1257–1264
Hanke M (1996) Limitations of the L-curve method in ill-posed problems [J]. BIT Numer Math 36(2):287–301
Hauk O (2004) Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data [J]. Neuroimage 21(4):1612–1621
Jeannerod M (1994) The representing brain: neural correlates of motor intention and imagery [J]. Behavioral & Brain Sciences 17(2):187–202
Jeannerod M (1995) Mental imagery in the motor context [J]. Neuropsychologia 33(11):1419–1432
Kamiński MJ, Blinowska KJ (1991) A new method of the description of the information ow in the brain structures [J]. Biol Cybern 65(3):203–210
Neuper C, Schlögl A, Pfurtscheller G (1999) Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery [J]. J Clin Neurophysiol 16(4):373–382
Pfurtscheller G, Nauper C, Flotzinger D et al (1997) EEG-based discrimination between imagination of right and left hand movement [J]. Electroencephalogr Clin Neurophysiol 103(6):642–651
Schlögl A, Lee F, Bischof H et al (2005) Characterization of four-class motor imagery EEG data for the BCI-competition 2005 [J]. J Neural Eng 2(4):L14–L22
Yang X, Dezhong Y (2015) The Basics of Neuroinformatics [M]. University of Electronic Science and Technology Press
Acknowledgments
This work was supported by grants from the National Natural Science Foundation of China (81171866), the National Key Basic Research Program of China (No.2014CB541602) and the Research Program of southwest hospital(SWH2014ZH03).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11042-022-13972-z
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Feng, Z., He, Q., Wang, L. et al. RETRACTED ARTICLE: EEG oscillatory patterns in the different processing phase during motor imagery. Multimed Tools Appl 79, 17101–17113 (2020). https://doi.org/10.1007/s11042-019-07763-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-07763-2