Skip to main content
SpringerLink
Log in

Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain–Computer Interface

  • Original Article
  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

This study aims to estimate the maximum power consumption that guarantees a thermally safe operation for a titanium-enclosed chest wall unit (CWU) subcutaneously implanted in the pre-pectoral area. This unit is a central piece of an envisioned fully-implantable bi-directional brain–computer interface (BD-BCI). To this end, we created a thermal simulation model using the finite element method implemented in COMSOL. We also performed a sensitivity analysis to ensure that our predictions were robust against the natural variation of physiological and environmental parameters. Based on this analysis, we predict that the CWU can consume between 378 and 538 mW of power without raising the surrounding tissue’s temperature above the thermal safety threshold of 2 \(^{\circ }\)C. This power budget should be sufficient to power all of the CWU’s basic functionalities, which include training the decoder, online decoding, wireless data transmission, and cortical stimulation. This power budget assessment provides an important specification for the design of a CWU—an integral part of a fully-implantable BD-BCI system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Arzuaga, P. Cardiac pacemakers: Past, present and future. IEEE Instrumentation and Measurement Magazine. 17(3):21–27, 2014.

    Article  Google Scholar 

  2. DiMarco, J. P. Implantable cardioverter-defibrillators. New England Journal of Medicine. 349(19):1836–1847, 2003.

    Article  CAS  PubMed  Google Scholar 

  3. George, M. S., H. A. Sackeim, A. J. Rush, L. B. Marangell, Z. Nahas, M. M. Husain, S. Lisanby, T. Burt, J. Goldman, and J. C. Ballenger. Vagus nerve stimulation: A new tool for brain research and therapy. Biological Psychiatry. 47(4):287–295, 2000.

    Article  CAS  PubMed  Google Scholar 

  4. Lozano, A. M., N. Lipsman, H. Bergman, P. Brown, S. Chabardes, J. W. Chang, K. Matthews, C. C. McIntyre, T. E. Schlaepfer, M. Schulder, Y. Temel, J. Volkmann, and J. K. Krauss. Deep brain stimulation: Current challenges and future directions. Nature Reviews Neurology. 15(3):148–160, 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Morrell, M. J. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology. 77(13):1295–1304, 2011.

    Article  PubMed  Google Scholar 

  6. Dewhirst, M. W., B. L. Viglianti, M. Lora-Michiels, M. Hanson, and P. J. Hoopes. Basic principles of thermal dosimetry and thermal thresholds for tissue damage from hyperthermia. International Journal of Hyperthermia. 19(3):267–294, 2003.

    Article  CAS  PubMed  Google Scholar 

  7. A. Mahajan, A. K. Bidhendi, P. T. Wang, C. M. McCrimmon, C. Y. Liu, Z. Nenadic, A. H. Do, and P. Heydari. A 64-channel ultra-low power bioelectric signal acquisition system for brain-computer interface. In: IEEE biomedical circuits and systems conference: engineering for healthy minds and able bodies, BioCAS 2015 - Proceedings, pp. 3–6, 2015.

  8. Karimi-Bidhendi, A., O. Malekzadeh-Arasteh, M. C. Lee, C. M. McCrimmon, P. T. Wang, A. Mahajan, C. Yu Liu, Z. Nenadic, A. H. Do, and P. Heydari. CMOS ultralow power brain signal acquisition front-ends: Design and human testing. IEEE Transactions on Biomedical Circuits and Systems. 11(5):1111–1122, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  9. O. Malekzadeh-Arasteh, H. Pu, J. Lim, C. Y. Liu, A. H. Do, Z. Nenadic, and P. Heydari. An energy-efficient CMOS dual-mode array architecture for high-density ECoG-based brain-machine interfaces. In: IEEE transactions on biomedical circuits and systems, pp. 1–11, 2019.

  10. Lee, M. C., A. Karimi-Bidhendi, O. Malekzadeh-Arasteh, P. T. Wang, A. H. Do, Z. Nenadic, and P. Heydari. A CMOS MedRadio transceiver with supply-modulated power saving technique for an implantable brain-machine interface system. IEEE Journal of Solid-State Circuits. 54(6):1541–1552, 2019.

    Article  Google Scholar 

  11. Wang, P. T., E. Camacho, M. Wang, Y. Li, S. J. Shaw, M. Armacost, H. Gong, D. Kramer, B. Lee, R. A. Andersen, C. Y. Liu, P. Heydari, Z. Nenadic, and A. H. Do. A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface. Journal of Neural Engineering. 16(6):066043, 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sohn, W. J., J. Lim, P. T. Wang, H. Pu, O. Malekzadeh-Arasteh, S. J. Shaw, M. Armacost, H. Gong, S. Kellis, R. A. Andersen, C. Y. Liu, P. Heydari, Z. Nenadic, and A. H. Do. Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research. Frontiers in Neuroscience. 16:1075971, 2023.

    Article  PubMed  PubMed Central  Google Scholar 

  13. T. Campi, S. Cruciani, V. De Santis, and M. Feliziani. EMF safety and thermal aspects in a pacemaker equipped with a wireless power transfer system working at low frequency. In: IEEE transactions on microwave theory and techniques, pp. 1–8, 2016.

  14. Mohsin, S. A. Concentration of the specific absorption rate around deep brain stimulation electrodes during MRI. Progress In Electromagnetics Research. 121:469–484, 2011.

    Article  Google Scholar 

  15. Winter, L., F. Seifert, L. Zilberti, M. Murbach, and B. Ittermann. MRI-related heating of implants and devices: A review. Journal of Magnetic Resonance Imaging. 53(6):1646–1665, 2021.

    Article  PubMed  Google Scholar 

  16. Kim, J., and Y. Rahmat-Samii. Implanted antennas inside a human body: Simulations, designs, and characterizations. IEEE Transactions on Microwave Theory and Techniques. 52(8):1934–1943, 2004.

    Article  Google Scholar 

  17. Narayanamoorthi, R. Modeling of capacitive resonant wireless power and data transfer to deep biomedical implants. IEEE Transactions on Components, Packaging and Manufacturing Technology. 9(7):1253–1263, 2019.

    Article  CAS  Google Scholar 

  18. Ibrahim, T. S., D. Abraham, and R. L. Rennaker. Electromagnetic power absorption and temperature changes due to brain machine interface operation. Annals of Biomedical Engineering. 35(5):825–834, 2007.

    Article  PubMed  Google Scholar 

  19. Sohee, K., P. Tathireddy, R. A. Normann, and F. Solzbacher. Thermal impact of an active 3-D microelectrode array implanted in the brain. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 15(4):493–501, 2007.

    Article  Google Scholar 

  20. Elwassif, M. M., Q. Kong, M. Vazquez, and M. Bikson. Bio-heat transfer model of deep brain stimulation-induced temperature changes. Journal of Neural Engineering. 3(4):306, 2006.

    Article  PubMed  Google Scholar 

  21. Opie, N. L., A. N. Burkitt, H. Meffin, and D. B. Grayden. Heating of the eye by a retinal prosthesis: Modeling, cadaver and in vivo study. IEEE Transactions on Biomedical Engineering. 59(2):339–345, 2012.

    Article  PubMed  Google Scholar 

  22. C. Serrano-Amenos, F. Hu, P. T. Wang, S. Kellis, R. A. Andersen, C. Y. Liu, P. Heydari, A. H. Do, and Z. Nenadic. Thermal analysis of a skull implant in brain-computer interfaces. In 2020 42nd Annual international conference of the IEEE engineering in medicine biology society (EMBC), pp. 3066–3069, 2020.

  23. Serrano-Amenos, C., P. Heydari, C. Y. Liu, A. H. Do, and Z. Nenadic. Power budget of a skull unit in a fully-implantable brain-computer interface: Bio-heat model. Transactions on neural systems and rehabilitation engineering. 31:4029–4039, 2023.

    Article  PubMed  Google Scholar 

  24. Wolf, P. D. Thermal considerations for the design of an implanted cortical brain-machine interface (BMI). In: Indwelling neural implants: Strategies for contending with the in vivo environment, edited by W. M. Reichert. Boca Raton: CRC Press/Taylor & Francis, 2008, pp. 1–20.

    Google Scholar 

  25. Rizk, M., I. Obeid, S. H. Callender, and P. D. Wolf. A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system. Journal of Neural Engineering. 4(3):309, 2007.

    Article  PubMed  Google Scholar 

  26. Haghjoo, M. Techniques of permanent pacemaker implantation, current issues and recent advances in pacemaker therapy. Cardiovascular Medicine. 1:4–10, 2012.

    Google Scholar 

  27. Sarica, C., C. Iorio-Morin, D. H. Aguirre-Padilla, A. Najjar, M. Paff, A. Fomenko, K. Yamamoto, A. Zemmar, N. Lipsman, G. M. Ibrahim, C. Hamani, M. Hodaie, A. M. Lozano, P. M. Renato, A. Fasano, and S. K. Kalia. Implantable pulse generators for deep brain stimulation: Challenges, complications, and strategies for practicality and longevity. Frontiers in Human Neuroscience. 15:7084, 2021.

    Article  Google Scholar 

  28. Laurent, A., F. Mistretta, D. Bottigioli, K. Dahel, C. Goujon, J. F. Nicolas, A. Hennino, and P. E. Laurent. Echographic measurement of skin thickness in adults by high frequency ultrasound to assess the appropriate microneedle length for intradermal delivery of vaccines. Vaccine. 25(34):6423–6430, 2007.

    Article  PubMed  Google Scholar 

  29. Störchle, P., W. Müller, M. Sengeis, S. Lackner, S. Holasek, and A. Fürhapter-Rieger. Measurement of mean subcutaneous fat thickness: Eight standardised ultrasound sites compared to 216 randomly selected sites. Scientific Reports. 8(1):16268, 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bueno, A. F., F. A. Lemos, M. E. Ferrareze, W. A. M. dos Santos, F. V. Veronese, and A. S. Dias. Muscle thickness of the pectoralis major and rectus abdominis and level of physical activity in chronic hemodialysis patients. Jornal Brasileiro de Nefrologia. 39(4):391, 2017.

    Article  PubMed  Google Scholar 

  31. Mohr, M., E. Abrams, C. Engel, W. B. Long, and M. Bottlang. Geometry of human ribs pertinent to orthopedic chest-wall reconstruction. Journal of Biomechanics. 40(6):1310–1317, 2007.

    Article  PubMed  Google Scholar 

  32. Sette, A. L., E. Seigneuret, F. Reymond, S. Chabardes, A. Castrioto, B. Boussat, E. Moro, P. François, and V. Fraix. Battery longevity of neurostimulators in Parkinson disease: A historic cohort study. Brain Stimulation. 12(4):851–857, 2019.

    Article  CAS  PubMed  Google Scholar 

  33. Pennes, H. H. Analysis of tissue and arterial blood temperatures in the resting human forearm. Journal of Applied Physiology. 1(2):93–122, 1948.

    Article  CAS  PubMed  Google Scholar 

  34. Duck, F. A. Thermal properties of tissue. In: Physical properties of tissues, Amsterdam: Elsevier, 1990, pp. 9–42.

    Chapter  Google Scholar 

  35. Gordon, R. G., R. B. Roemer, and S. M. Horvath. A mathematical model of the human temperature regulatory system-transient cold exposure response. IEEE Transactions on Biomedical Engineering. BME–23(6):434–444, 1976.

    Article  Google Scholar 

  36. Wang, Z., Z. Ying, A. Bosy-Westphal, J. Zhang, B. Schautz, W. Later, S. B. Heymsfield, and M. J. Müller. Specific metabolic rates of major organs and tissues across adulthood: Evaluation by mechanistic model of resting energy expenditure. The American Journal of Clinical Nutrition. 92(6):1369–1377, 2010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Heinonen, I., M. Bucci, J. Kemppainen, J. Knuuti, P. Nuutila, R. Boushel, and K. K. Kalliokoski. Regulation of subcutaneous adipose tissue blood flow during exercise in humans. Journal of Applied Physiology. 112(6):1059–1063, 2012.

    Article  PubMed  Google Scholar 

  38. Heinonen, I., R. Boushel, Y. Hellsten, and K. Kalliokoski. Regulation of bone blood flow in humans: The role of nitric oxide, prostaglandins, and adenosine. Scandinavian Journal of Medicine & Science in Sports. 28(5):1552–1558, 2018.

    Article  CAS  Google Scholar 

  39. Workman, J. M., and B. W. Armstrong. Oxygen cost of treadmill walking. Journal of Applied Physiology. 18(4):798–803, 1963.

    Article  CAS  PubMed  Google Scholar 

  40. Grimby, G., E. Häggendal, and B. Saltin. Local xenon 133 clearance from the quadriceps muscle during exercise in man. Journal of Applied Physiology. 22(2):305–310, 1967.

    Article  CAS  PubMed  Google Scholar 

  41. Kovacs, G., A. Berghold, S. Scheidl, and H. Olschewski. Pulmonary arterial pressure during rest and exercise in healthy subjects: A systematic review. European Respiratory Journal. 34(4):888–894, 2009.

    Article  CAS  PubMed  Google Scholar 

  42. Argiento, P., N. Chesler, M. Mule, M. D’Alto, E. Bossone, P. Unger, and R. Naeije. Exercise stress echocardiography for the study of the pulmonary circulation. European Respiratory Journal. 35(6):1273–1278, 2010.

    Article  CAS  PubMed  Google Scholar 

  43. Lee, S. M. C., W. J. Williams, and S. M. Schneider. Role of skin blood flow and sweating rate in exercise thermoregulation after bed rest. Journal of Applied Physiology. 92(5):2026–2034, 2002.

    Article  PubMed  Google Scholar 

  44. Guyton, A. C. Human physiology and mechanisms of disease, 6th ed. London: W B Saunders, 1996.

    Google Scholar 

  45. Barrett, K. E., S. M. Barman, J. Yuan, and H. L. Brooks. Ganong’s review of medical physiology, 26th ed. McGraw-Hill Education/Medical, 2019.

    Google Scholar 

  46. Mapleson, W. W. An electric analogue for uptake and exchange of inert gases and other agents. Journal of Applied Physiology. 18(1):197–204, 1963.

    Article  CAS  PubMed  Google Scholar 

  47. Cowles, A. L., H. H. Borgstedt, and A. J. Gillies. Tissue weights and rates of blood flow in man for the prediction of anesthetic uptake and distribution. Anesthesiology. 35(5):523–526, 1971.

    Article  CAS  PubMed  Google Scholar 

  48. Williams, L. R., and R. W. Leggett. Reference values for resting blood flow to organs of man. Clinical Physics and Physiological Measurement. 10(3):187–217, 1989.

    Article  CAS  PubMed  Google Scholar 

  49. McIntosh, R. L., and V. Aderson. A comprehensive tissue properties database provided for the thermal assessment of a human at rest. Biophysical Reviews and Letters. 05(03):129–151, 2010.

    Article  Google Scholar 

  50. Powell, R. W., and R. P. Tye. The thermal and electrical conductivity of titanium and its alloys. Journal of the Less-Common Metals. 3(3):226–233, 1961.

    Article  CAS  Google Scholar 

  51. Beirão, S. G. S., A. P. Ribeiro, M. J. Lourenço, F. J. V. Santos, and C. A. Nieto De Castro. Thermal conductivity of humid air. International Journal of Thermophysics. 33(8–9):1686–1703, 2012.

    Article  Google Scholar 

  52. S. P. Gurrum, D. R. Edwards, T. Marchand-Golder, J. Akiyama, S. Yokoya, J. F. Drouard, and F. Dahan. Generic thermal analysis for phone and tablet systems. In 2012 IEEE 62nd electronic components and technology conference, pp. 1488–1492. IEEE, 2012.

  53. Nirale, V. Evaluation of effective thermal conductivity in PCB. IJRST. 3:174–179, 2016.

    Google Scholar 

  54. Kosky, P., R. Balmer, W. Keat, and G. Wise. Mechanical engineering. In: Exploring engineering, Amsterdam: Elsevier, 2021, pp. 317–340.

    Chapter  Google Scholar 

  55. Del Bene, V. E. Temperature. In: Clinical methods: The history, physical, and laboratory examinations,Boston: Butterworths, 1990.

    Google Scholar 

  56. Best, C. H., N. B. Taylor, and J. R. Brobeck. Best & Taylor’s physiological basis of medical practice. Williams & Wilkins, 1979.

    Google Scholar 

  57. Downing, D. J., R. H. Gardner, and F. O. Hoffman. An examination of response-surface methodologies for uncertainty analysis in assessment models. Technometrics. 27(2):151–163, 1985.

    Article  Google Scholar 

  58. Lazzi, G. Thermal effects of bioimplants. IEEE Engineering in Medicine and Biology Magazine. 24(5):75–81, 2005.

    Article  PubMed  Google Scholar 

  59. Yao, C., J. Lu, and T. J. Webster. Titanium and cobalt-chromium alloys for hips and knees. In: Biomaterials for artificial organs, edited by M. Lysaght, and T. J. Webster. Amsterdam: Elsevier, 2011, pp. 34–55.

    Chapter  Google Scholar 

  60. Boyer, R., G. Welsch, and E. W. Collings. Materials properties handbook - titanium alloys. ASM International, 1994.

    Google Scholar 

  61. Seo, J., J. Jeon, J.-H. Lee, and S. Kim. Thermal performance analysis according to wood flooring structure for energy conservation in radiant floor heating systems. Energy and Buildings. 43(8):2039–2042, 2011.

    Article  Google Scholar 

  62. Ohmite. Characteristics for ohmite axial-leaded resistor materials. https://www.ohmite.com/axial-resistors-material-characteristics/. Accessed: 2022-12-06.

  63. Technical Products, Inc. https://www.technicalproductsinc.com. Accessed: 2022-12-06.

  64. Desai, P. D. Thermodynamic properties of titanium. International Journal of Thermophysics. 8(6):781–794, 1987.

    Article  CAS  Google Scholar 

  65. Cubo, R., M. Fahlström, E. Jiltsova, H. Andersson, and A. Medvedev. Calculating deep brain stimulation amplitudes and power consumption by constrained optimization. Journal of Neural Engineering.16(1):016020, 2019.

    Article  PubMed  Google Scholar 

  66. R. B. Strother, G. B. Trophe, J. J. Mrva, and D. R. Pack. Implantable pulse generator systemis and methods for providing functional and/or therapeutc stmulation of muscles and/or nerves and/or central nervous system tissue, 2007.

  67. Davies, C. R., F. Fukumura, K. Fukamachi, K. Muramoto, S. C. Himley, A. Massiello, J.-F. Chen, and H. Harasaki. Adaptation of tissue to a chronic heat load. ASAIO Journal. 40(3):M514–M517, 1994.

    Article  CAS  PubMed  Google Scholar 

  68. D. Do. personal communication.

  69. Okazaki, Y., C. R. Davies, T. Matsuyoshi, K. Fukamachi, K. E. Wika, and H. Harasaki. Heat from an implanted power source is mainly dissipated by blood perfusion. ASAIO Journal American Society for Artificial Internal Organs. 43(5):585–588, 1997.

    Google Scholar 

  70. Matsuki, H., T. Matsuzaki, and T. Satoh. Simulations of temperature rise on transcutaneous energy transmission by non-contact energy transmitting coils. IEEE Transactions on Magnetics. 29(6):3334–3336, 1993.

    Article  Google Scholar 

  71. Walckiers, G., B. Fuchs, J.-P. Thiran, J. R. Mosig, and C. Pollo. Influence of the implanted pulse generator as reference electrode in finite element model of monopolar deep brain stimulation. Journal of Neuroscience Methods. 186(1):90–96, 2010.

    Article  PubMed  Google Scholar 

  72. Yousif, N., and X. Liu. Investigating the depth electrode-brain interface in deep brain stimulation using finite element models with graded complexity in structure and solution. Journal of Neuroscience Methods. 184(1):142–151, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Pelot, N. A., B. J. Thio, and W. M. Grill. Modeling current sources for neural stimulation in COMSOL. Frontiers in Computational Neuroscience. 12:40, 2018.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by the National Science Foundation (Award No. 1646275). Claudia Serrano-Amenos also acknowledges the support from the Balsells Fellowship.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, UCI, Irvine, CA, 92697, USA

    Claudia Serrano-Amenos, Po T. Wang & Zoran Nenadic

  2. Department of Mechanical and Aerospace Engineering, UCI, Irvine, CA, 92697, USA

    Frank Hu

  3. Department of Electrical Engineering and Computer Science, UCI, Irvine, CA, 92697, USA

    Payam Heydari

  4. Department of Neurology, UCI, Irvine, CA, 92697, USA

    An H. Do

Authors
  1. Claudia Serrano-Amenos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Po T. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Payam Heydari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. An H. Do
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zoran Nenadic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudia Serrano-Amenos.

Ethics declarations

Conflict of interest

No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript.

Additional information

Associate Editor Stefan M. Duma oversaw the review of this article.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Serrano-Amenos, C., Hu, F., Wang, P.T. et al. Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain–Computer Interface. Ann Biomed Eng (2024). https://doi.org/10.1007/s10439-024-03528-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10439-024-03528-7

Keywords

Search

Navigation