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Towards Online Functional Brain Mapping and Monitoring During Awake Craniotomy Surgery Using ECoG-Based Brain-Surgeon Interface (BSI)

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Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

During brain surgery, functional brain mapping is critical, and the time needed for the procedure should be reduced to the minimum in order to avoid risks for the patient. In this project, we extend the traditional concept of BCI for communication and control between brain and external devices, to the concept of Brain-Surgeon Interface (BSI) in order to establish an interactive channel between the patient’s brain and the surgeon, with the ultimate goal of a high quality and precise brain surgery. Compared with “intraoperative fMRI”, which is expensive and time-consuming, or cortical electrical stimulation, which may cause epilepsy and brain swelling during the surgery, the proposed ECoG-based BSI system works in an online scenario, and the mapping time can be significantly reduced to a half minute for both motor and sensory cortex. Three signal modalities were used for functional brain mapping: movement related cortical potential, steady-state somatosensory evoked potential, event-related (de)synchronization. The proposed BSI system may provide a considerable advantage for clinical brain surgery applications.

Keywords

Brain- surgeon interface Brain surgery MRCP Oscillatory dynamics SSSEP ECoG 

References

  1. 1.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791Google Scholar
  2. 2.
    Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U. S. A. 101(51):17849–17854CrossRefGoogle Scholar
  3. 3.
    Daly JJ, Wolpaw JR (2008) Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 7(11):1032–1043CrossRefGoogle Scholar
  4. 4.
    Kübler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, Mcfarland DJ, Birbaumer N, Wolpaw JR (2005) Patients with ALS can use sensorimotor rhythms to operate a brainGoogle Scholar
  5. 5.
    Pichiorri F, Morone G, Petti M, Toppi J, Pisotta I, Molinari M, Paolucci S, Inghilleri M, Astolfi L, Cincotti F, Mattia D (2015) Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol 77(5):851–865CrossRefGoogle Scholar
  6. 6.
    Xu R, Jiang N, Mrachacz-Kersting N, Lin C (2014) A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity 61(7):2092–2101Google Scholar
  7. 7.
    Jiang N, Gizzi L, Mrachacz-Kersting N, Dremstrup K, Farina D (2014) A brain-computer interface for single-trial detection of gait initiation from movement related cortical potentials. Clin NeurophysiolGoogle Scholar
  8. 8.
    Mrachacz-Kersting N, Jiang N, Stevenson AJT, Niazi IK, Kostic V, Pavlovic A, Radovanovic S, Djuric-Jovicic M, Agosta F, Dremstrup K, Farina D (2015) Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J Neurophysiol. doi:  10.1152/jn.00918.2015
  9. 9.
    Behrens E, Schramm J, Zentner J, König R (1997) Surgical and neurological complications in a series of 708 epilepsy surgery procedures. Neurosurgery 41(1):1–9CrossRefGoogle Scholar
  10. 10.
    Jacobs J, Zijlmans M, Zelmann R, Chatillon C-É, Hall J, Olivier A, Dubeau F, Gotman J (2010) High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann Neurol 67(2):209–220CrossRefGoogle Scholar
  11. 11.
    Jeha LE, Najm I, Bingaman W, Dinner D, Widdess-Walsh P, Lüders H (2007) Surgical outcome and prognostic factors of frontal lobe epilepsy surgery. Brain 130(2):574–584CrossRefGoogle Scholar
  12. 12.
    Salanova V, Markand O, Worth R (2002) Temporal lobe epilepsy surgery: outcome, complications, and late mortality rate in 215 patients. Epilepsia 43(2):170–174CrossRefGoogle Scholar
  13. 13.
    Picht T, Schmidt S, Brandt S, Frey D, Hannula H, Neuvonen T, Karhu J, Vajkoczy P, Suess O (2011) Preoperative functional mapping for rolandic brain tumor surgery: comparison of navigated transcranial magnetic stimulation to direct cortical stimulation. Neurosurgery 69(3):581–589CrossRefGoogle Scholar
  14. 14.
    Duffau H (2005) Lessons from brain mapping in surgery for low-grade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol 4(8):476–486CrossRefGoogle Scholar
  15. 15.
    Schiffbauer H, Berger MS, Ferrari P, Freudenstein D, Rowley HA, Roberts TPL (2002) Preoperative magnetic source imaging for brain tumor surgery: a quantitative comparison with intraoperative sensory and motor mapping. J Neurosurg 97(6):1333–1342CrossRefGoogle Scholar
  16. 16.
    Shinoura N, Yamada R, Kodama T, Suzuki Y, Takahashi M, Yagi K (2005) Preoperative fMRI, tractography and continuous task during awake surgery for maintenance of motor function following surgical resection of metastatic tumor spread to the primary motor area. min-Minimally Invasive Neurosurg 48(2):85–90Google Scholar
  17. 17.
    Adcock JE, Wise RG, Oxbury JM, Oxbury SM, Matthews PM (2003) Quantitative fMRI assessment of the differences in lateralization of language-related brain activation in patients with temporal lobe epilepsy. Neuroimage 18(2):423–438CrossRefGoogle Scholar
  18. 18.
    Szelényi A, Bello L, Duffau H, Fava E, Feigl GC, Galanda M, Neuloh G, Signorelli F, Sala F (2010) Intraoperative electrical stimulation in awake craniotomy: methodological aspects of current practice. Neurosurg Focus 28(2):E7CrossRefGoogle Scholar
  19. 19.
    Lu J-F, Zhang H, Wu J-S, Yao C-J, Zhuang D-X, Qiu T-M, Jia W-B, Mao Y, Zhou L-F (2013) Awake intraoperative functional MRI (ai-fMRI) for mapping the eloquent cortex: is it possible in awake craniotomy? NeuroImage Clin 2:132–142CrossRefGoogle Scholar
  20. 20.
    Niazi IK, Jiang N, Tiberghien O, Nielsen JF, Dremstrup K, Farina D (2011) Detection of movement intention from single-trial movement-related cortical potentials. J Neural Eng 8(6):66009CrossRefGoogle Scholar
  21. 21.
    Xu R, Jiang N, Lin C, Mrachacz-kersting N, Dremstrup K, Farina D (2014) Enhanced low-latency detection of motor intention interface applications 61(2):288–296Google Scholar
  22. 22.
    Pfurtscheller G, da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857CrossRefGoogle Scholar
  23. 23.
    Neuper C, Wörtz M, Pfurtscheller G (2006) ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res 159:211–222CrossRefGoogle Scholar
  24. 24.
    Müller-Putz GR, Scherer R, Neuper C, Pfurtscheller G (2006) Steady-state somatosensory evoked potentials: Suitable brain signals for brain-computer interfaces? IEEE Trans Neural Syst Rehabil Eng 14(1):30–37CrossRefGoogle Scholar
  25. 25.
    Breitwieser C, Kaiser V, Neuper C, Müller-Putz GR (2012) Stability and distribution of steady-state somatosensory evoked potentials elicited by vibro-tactile stimulation. Med Biol Eng Comput 50(4):347–357CrossRefGoogle Scholar
  26. 26.
    Houdayer E, Labyt E, Cassim F, Bourriez JL, Derambure P (2006) Relationship between event-related beta synchronization and afferent inputs: Analysis of finger movement and peripheral nerve stimulations. Clin Neurophysiol 117(3):628–636CrossRefGoogle Scholar
  27. 27.
    Houdayer E, Degardin A, Salleron J, Bourriez JL, Defebvre L, Cassim F, Derambure P (2012) Movement preparation and cortical processing of afferent inputs in cortical tremor: an event-related (de)synchronization (ERD/ERS) study. Clin Neurophysiol 123(6):1207–1215Google Scholar
  28. 28.
    Yao L, Meng J, Zhang D, Sheng X, Zhu X (2013) Selective sensation based brain-computer interface via mechanical vibrotactile stimulation. PLoS One 8(6)Google Scholar
  29. 29.
    Yao L, Meng J, Zhang D, Sheng X, Zhu X (2014) Combining motor imagery with selective sensation toward a hybrid-modality BCI. IEEE Trans Biomed Eng 61(8):2304–2312CrossRefGoogle Scholar
  30. 30.
    Yao L, Meng J, Sheng X, Zhang D, Zhu X (2015) A novel calibration and task guidance framework for motor imagery BCI via a tendon vibration induced sensation with kinesthesia illusion. J Neural Eng 12(1):16005CrossRefGoogle Scholar
  31. 31.
    Yao L, Sheng X, Zhang D, Jiang N, Farina D, Zhu X (2016) A BCI system based on somatosensory attentional orientation. IEEE Trans Neural Syst Rehabil Eng 4320(c):1–1Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Department of Systems Design EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.State Key Lab of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Department of NeurosurgeryHuashan Hospital, Fudan UniversityShanghaiChina
  4. 4.Shenzhen Institutes of Advanced Technology Chinese Academy of ScienceBeijingChina
  5. 5.Universita Di BresciaBrescia, LombardyItaly
  6. 6.Department of Health Science and TechnologyCenter for Sensory-Motor Interaction, Aalborg UniversityAalborgDenmark
  7. 7.Department of BioengineeringImperial College LondonLondonUK

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