Skip to main content

An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 656))

Abstract

This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues. The results obtained demonstrate the advantage of requiring the subjects to perform motor actions following the trials of imagery. By introducing motor actions in the protocol, the subjects are able to perform actual motor planning, rather than just visualizing the motor movement, thus greatly improving the ease with which the motor movements can be imagined. This study also probes the added advantage of administering somatosensory cues in the subject, as opposed to the conventional auditory/visual cues. These changes in the protocol show promise in terms of the aptness of the spatial filters obtained by the data, on the application of the well-known common spatial pattern (CSP) algorithms. The regions highlighted by the spatial filters are more localized and consistent across the subjects when the protocol is augmented with somatosensory stimuli. Hence, we suggest that this may prove to be a better EEG acquisition protocol for detecting brain activation in response to intended motor commands in (clinically) paralyzed/locked-in patients.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Batula AM, Mark JA, Kim YE, Ayaz H (2017) Comparison of brain activation during motor imagery and motor movement using fNIRS. Comput Intell Neurosci 2017

    Google Scholar 

  2. Blankertz B, Müller KR, Curio G, Vaughan TM, Schalk G, Wolpaw JR, Schlögl A, Neuper C, Pfurtscheller G, Hinterberger T et al (2004) The BCI competition 2003. IEEE Trans Biomed Eng 51(6):1044–51

    Article  Google Scholar 

  3. Bonassi G, Biggio M, Bisio A, Ruggeri P, Bove M, Avanzino L (2017) Provision of somatosensory inputs during motor imagery enhances learning-induced plasticity in human motor cortex. Sci Rep 7(1):9300

    Article  Google Scholar 

  4. Brandl S, Höhne J, Müller KR, Samek W (2015) Bringing BCI into everyday life: motor imagery in a pseudo realistic environment. In: 2015 7th international IEEE/EMBS conference on neural engineering (NER). IEEE, pp 224–227

    Google Scholar 

  5. Brandl S, Müller KR, Samek W (2016) Alternative CSP approaches for multimodal distributed BCI data. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 003742–003747

    Google Scholar 

  6. Bundy DT, Souders L, Baranyai K, Leonard L, Schalk G, Coker R, Moran DW, Huskey T, Leuthardt EC (2017) Contralesional brain-computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors. Stroke 48(7):1908–1915

    Article  Google Scholar 

  7. Claassen J, Doyle K, Matory A, Couch C, Burger KM, Velazquez A, Okonkwo JU, King JR, Park S, Agarwal S et al (2019) Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med 380(26):2497–2505

    Article  Google Scholar 

  8. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  Google Scholar 

  9. Hanakawa T, Immisch I, Toma K, Dimyan MA, Van Gelderen P, Hallett M (2003) Functional properties of brain areas associated with motor execution and imagery. J Neurophysiol 89(2):989–1002

    Article  Google Scholar 

  10. Jerrin TP, Ramakrishnan A, Ananthapadmanabha T (2019) A novel deep learning architecture for decoding imagined speech from EEG. In: IEEE Austria international biomedical engineering conference (AIBEC 2019). IEEE

    Google Scholar 

  11. Kropotov JD (2010) Quantitative EEG, event-related potentials and neurotherapy. Academic, San Diego

    Google Scholar 

  12. Kropotov JD (2016) Functional neuromarkers for psychiatry: applications for diagnosis and treatment. Academic, San Diego

    Google Scholar 

  13. Lotte F, Guan C (2010) Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 58(2):355–362

    Article  Google Scholar 

  14. McFarland DJ, Miner LA, Vaughan TM, Wolpaw JR (2000) Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr 12(3):177–186

    Article  Google Scholar 

  15. Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain-computer communication. Proc IEEE 89(7):1123–1134

    Article  Google Scholar 

  16. Pfurtscheller G, Brunner C, Schlögl A, Da Silva FL (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1):153–159

    Article  Google Scholar 

  17. Saimpont A, Malouin F, Tousignant B, Jackson PL (2013) Motor imagery and aging. J Mot Behav 45(1):21–28

    Article  Google Scholar 

  18. Sannelli C, Dickhaus T, Halder S, Hammer EM, Müller KR, Blankertz B (2010) On optimal channel configurations for SMR-based brain-computer interfaces. Brain Topogr 23(2):186–193

    Article  Google Scholar 

  19. Sharma N, Jones PS, Carpenter T, Baron JC (2008) Mapping the involvement of BA 4a and 4p during motor imagery. Neuroimage 41(1):92–99

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jerrin Thomas Panachakel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Panachakel, J.T., Vinayak, N.N., Nunna, M., Ramakrishnan, A.G., Sharma, K. (2020). An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation. In: Jayakumari, J., Karagiannidis, G., Ma, M., Hossain, S. (eds) Advances in Communication Systems and Networks . Lecture Notes in Electrical Engineering, vol 656. Springer, Singapore. https://doi.org/10.1007/978-981-15-3992-3_22

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3992-3_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3991-6

  • Online ISBN: 978-981-15-3992-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics