Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Predictive classification of self-paced upper-limb analytical movements with EEG

Abstract

The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5 % was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3 %). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Antelis JM, Montesano L, Ramos Murguialday A, Birbaumer N, Minguez J (2013) On the usage of linear regression models to reconstruct limb kinematics from low frequency EEG signals. PLoS ONE 8:e61976

  2. 2.

    Bai O, Lin P, Vorbach S, Li J, Furlani S, Hallett M (2007) Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG. Clin Neurophysiol 118:2637–2655

  3. 3.

    Bai O, Mari Z, Vorbach S, Hallett M (2005) Asymmetric spatiotemporal patterns of event-related desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study. Clin Neurophysiol 116:1213–1221

  4. 4.

    Bai O, Rathi V, Lin P, Huang D, Battapady H, Fei D-Y, Schneider L, Houdayer E, Chen X, Hallett M (2011) Prediction of human voluntary movement before it occurs. Clin Neurophysiol 122(2):364–372

  5. 5.

    Blankertz B (2008) Invariant common spatial patterns: alleviating nonstationarities in brain–computer interfacing. Adv Neural Inf Process Syst 20:1–12

  6. 6.

    Bradberry TJ, Gentili RJ, Contreras-Vidal JL (2010) Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. J Neurosci 30:3432–3437

  7. 7.

    Buzsáki G, Draguhn A. (2004) Neuronal oscillations in cortical networks. Science 304:1926–1929

  8. 8.

    Caracillo RC, Castro MCF (2013) Classification of executed upper limb movements by means of EEG. 2013 ISSNIP biosignals biorobotics conf biosignals robot better safer living, pp 1–6

  9. 9.

    Daly JJ, Wolpaw JR (2008) Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 7:1032–1043

  10. 10.

    Demandt E, Mehring C, Vogt K, Schulze-Bonhage A, Aertsen A, Ball T (2012) Reaching movement onset- and end-related characteristics of EEG spectral power modulations. Front Neurosci 6:65

  11. 11.

    Deng J, Yao J, Dewald JP (2005) Classification of the intention to generate a shoulder versus elbow torque by means of a time-frequency synthesized spatial patterns BCI algorithm. J Neural Eng 2:131–138

  12. 12.

    Derambure P, Defebvre L, Dujardin K, Bourriez JL, Jacquesson JM, Destee A, Guieu JD (1993) Effect of aging on the spatio-temporal pattern of event-related desynchronization during a voluntary movement. Electroencephalogr Clin Neurophysiol 89:197–203

  13. 13.

    Desmurget M, Sirigu A, Bernard C (2009) A parietal-premotor network for movement intention and motor awareness. Trends Cogn Sci 13:411–419

  14. 14.

    Engel AK, Fries P (2010) Beta-band oscillations–signalling the status quo? Curr Opin Neurobiol 20:156–165

  15. 15.

    Gu Y, do Nascimento OF, Lucas M-F, Farina D. (2009) Identification of task parameters from movement-related cortical potentials. Med Biol Eng Comput 47:1257–1264

  16. 16.

    Hammon P, Makeig S, Poizner H, Todorov E, De Sa V (2008) Predicting reaching targets from human EEG. IEEE Signal Process Mag 25:69–77

  17. 17.

    Hjorth B (1975) An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalogr Clin Neurophysiol 39:526–530

  18. 18.

    Ibáñez J, Serrano JI, del Castillo MD, Gallego JA, Rocon E (2013) Online detector of movement intention based on EEG. Application in tremor patients. Biomed Signal Process Control 8:822–829

  19. 19.

    Ibáñez J, Serrano JI, del Castillo MD, Monge-Pereira E, Molina-Rueda F, Alguacil-Diego I, Pons JL (2014) Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials. J Neural Eng 11:056009

  20. 20.

    Liao K, Xiao R, Gonzalez J, Ding L (2014) Decoding individual finger movements from one hand using human EEG signals. PLoS ONE 9:e85192

  21. 21.

    Libet B, Wright E, Gleason C (1982) Readiness-potentials preceding unrestricted “spontaneous” vs. pre-planned voluntary acts. Electroencephalogr Clin Neurophysiol 54:322–335

  22. 22.

    Lopez-Larraz E, Montesano L, Gil-Agudo A, Minguez J (2014) Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates. J Neuroeng Rehabil 11:153

  23. 23.

    Morash V, Bai O, Furlani S, Lin P, Hallett M (2008) Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries. Clin Neurophysiol 119:2570–2578

  24. 24.

    Neuper C, Wörtz M, Pfurtscheller G (2006) ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res 159:211–222

  25. 25.

    Niazi I, Mrachacz-Kersting N, Jiang N, Dremstrup K, Farina D (2012) Peripheral electrical stimulation triggered by self-paced detection of motor intention enhances motor evoked potentials. IEEE Trans Neural Syst Rehabil Eng 20:595–604

  26. 26.

    NN: RapidMiner—Manual. 2010 by Rapid-I GmbH. Online resource: http://docs.rapid-i.com/files/rapidminer/rapidminer-5.0-manual-english_v1.0.pdf. Last visit: June 30, 2013

  27. 27.

    Norman RW, Komi PV (1979) Electromechanical delay in skeletal muscle under normal movement conditions. Acta Physiol Scand 106:241–248

  28. 28.

    Obhi SS, Planetta PJ, Scantlebury J (2009) On the signals underlying conscious awareness of action. Cognition 110:65–73

  29. 29.

    Oby ER, Ethier C, Miller LE (2013) Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions. J Neurophysiol 109:666–678

  30. 30.

    Pfurtscheller G, Brunner C, Schlögl A, da Silva FHL, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31:153–159

  31. 31.

    Pfurtscheller G, Graimann B, Huggins JE, Levine SP, Schuh LA (2003) Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clin Neurophysiol 114:1226–1236

  32. 32.

    Pfurtscheller G, da Silva FHL (1999) Event-related EEG/EMG Synchronization and Desynchronization: basic principles. Clin Neurophysiol 110:1842–1857

  33. 33.

    Pfurtscheller G, Solis-Escalante T (2009) Could the beta rebound in the EEG be suitable to realize a “brain switch”? Clin Neurophysiol 120:24–29

  34. 34.

    Salmelin R, Hámáaláinen M, Kajola M, Hari R (1995) Functional segregation of movement-related rhythmic activity in the human brain. Neuroimage 2:237–243

  35. 35.

    Serrien DJ, Strens LHA, Cassidy MJ, Thompson AJ, Brown P (2004) Functional significance of the ipsilateral hemisphere during movement of the affected hand after stroke. Exp Neurol 190:425–432

  36. 36.

    Shenoy P, Krauledat M, Blankertz B, Rao RPN, Müller KR (2006) Towards adaptive classification for BCI. J Neural Eng 3:R13–R23

  37. 37.

    Sburlea AI, Montesano L, Minguez J (2015) Continuous detection of the self-initiated walking pre-movement state from EEG correlates without sessionto-session recalibration. J Neural Eng 12:036007

  38. 38.

    Stepien M, Conradi J, Waterstraat G, Hohlefeld FU, Curio G, Nikulin VV (2010) Event-related desynchronization of sensorimotor EEG rhythms in hemiparetic patients with acute stroke. Neurosci Lett 488(1):17–21

  39. 39.

    Urbano A, Babiloni C, Onorati P, Babiloni F (1996) Human cortical activity related to unilateral movements. A high resolution EEG study. Neuroreport 8:203–206

  40. 40.

    Wiese H, Stude P, Nebel K, Osenberg D, Völzke V, Ischebeck W, Stolke D, Diener HC, Keidel M (2004) Impaired movement-related potentials in acute frontal traumatic brain injury. Clin Neurophysiol 115:289–298

Download references

Acknowledgments

This work has been funded by grant from the Spanish Ministry of Science and Innovation CONSOLIDER INGENIO, project HYPER (Hybrid NeuroProsthetic and NeuroRobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders, CSD2009-00067), from Proyectos Cero of FGCSIC, Obra Social la Caixa, CSIC, and from the project PIE 201050E087.

Author information

Correspondence to Jaime Ibáñez.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ibáñez, J., Serrano, J.I., del Castillo, M.D. et al. Predictive classification of self-paced upper-limb analytical movements with EEG. Med Biol Eng Comput 53, 1201–1210 (2015). https://doi.org/10.1007/s11517-015-1311-x

Download citation

Keywords

  • Brain–computer interface
  • Electroencephalography
  • Voluntary movements
  • Genetic algorithms
  • Data mining