Skip to main content
SpringerLink
Log in

Human Intracranial Cognitive Neurophysiology

  • Protocol
  • First Online:
Electrophysiological Recording Techniques

Part of the book series: Neuromethods ((NM,volume 192))

  • 563 Accesses

Abstract

The neurophysiological mechanisms that enable cognitive functions are typically studied noninvasively in humans using scalp magnetic resonance imaging (MRI) and magneto- or electroencephalography (M/EEG) or invasively in rodents or nonhuman primates. Intracranial EEG as recorded in pharmacoresistant epilepsy patients who undergo evaluation for resective epilepsy surgery provides a unique approach to bridge the gap between noninvasive in humans and invasive studies in rodents and nonhuman primates. In recent years, iEEG has provided important insights into the functional architecture of human cognitive systems. Here, the principles of successfully conducting intracranial experiments in humans are discussed, with a particular focus on implementing recording setups and analyzing the data. The high spatiotemporal resolution of iEEG provides a number of opportunities and challenges, which are outlined alongside potential solutions. Collectively, intracranial cognitive neurophysiology is a rapidly progressing field providing key insights into the organization of human cognitive systems.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Institutional subscriptions

Similar content being viewed by others

References

  1. D’Esposito M (2010) Why methods matter in the study of the biological basis of the mind: a behavioral neurologist’s perspective. In: Reuter-Lorenz P, Baynes K, Mangun GR, Phelps EA (eds) The Cognitive neuroscience of mind: a tribute to Michael Gazzaniga. MIT Press, Cambridge, London, pp 203–221

    Chapter  Google Scholar 

  2. Szczepanski SM, Knight RT (2014) Insights into human behavior from lesions to the prefrontal cortex. Neuron 83:1002–1018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Parvizi J, Kastner S (2018) Promises and limitations of human intracranial electroencephalography. Nat Neurosci 21:474–483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Corkin S (2002) What’s new with the amnesic patient H.M.? Nat Rev Neurosci 3:153

    Article  CAS  PubMed  Google Scholar 

  5. Squire LR (2009) The legacy of patient H.M. for neuroscience. Neuron 61:6–9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Vaidya AR, Pujara MS, Petrides M et al (2019) Lesion studies in contemporary neuroscience. Trends Cogn Sci (Regul Ed) 23:653–671

    Article  Google Scholar 

  7. Helfrich RF, Knight RT (2016) Oscillatory dynamics of prefrontal cognitive control. Trends Cogn Sci (Regul Ed) 20:916–930

    Article  Google Scholar 

  8. Miller EK, Cohen JD (2001) An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24:167–202

    Article  CAS  PubMed  Google Scholar 

  9. Engel AK, Fries P, Singer W (2001) Dynamic predictions: oscillations and synchrony in top-down processing. Nat Rev Neurosci 2:704–716

    Article  CAS  PubMed  Google Scholar 

  10. Singer W, Gray CM (1995) Visual feature integration and the temporal correlation hypothesis. Annu Rev Neurosci 18:555–586

    Article  CAS  PubMed  Google Scholar 

  11. Buzsaki G (2006) Rhythms of the brain. Oxford University Press

    Book  Google Scholar 

  12. Varela F, Lachaux JP, Rodriguez E et al (2001) The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2:229–239

    Article  CAS  PubMed  Google Scholar 

  13. Buzsáki G, Anastassiou CA, Koch C (2012) The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes. Nat Rev Neurosci 13:407–420

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Pesaran B, Vinck M, Einevoll GT et al (2018) Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat Neurosci 21:903–919

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Engel AK, Moll CKE, Fried I et al (2005) Invasive recordings from the human brain: clinical insights and beyond. Nat Rev Neurosci 6:35–47

    Article  CAS  PubMed  Google Scholar 

  16. Bronstein JM, Tagliati M, Alterman RL et al (2011) Deep brain stimulation for Parkinson disease: an expert consensus and review of key issues. Arch Neurol 68:165–165

    Article  PubMed  Google Scholar 

  17. Weiss D, Klotz R, Govindan RB et al (2015) Subthalamic stimulation modulates cortical motor network activity and synchronization in Parkinson’s disease. Brain 138:679–693

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zavala BA, Jang AI, Zaghloul KA (2017) Human subthalamic nucleus activity during non-motor decision making. eLife 6. https://elifesciences.org/articles/31007

  19. Helfrich RF, Fiebelkorn IC, Szczepanski SM et al (2018) Neural mechanisms of sustained attention are rhythmic. Neuron 99:854–865.e5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Helfrich RF, Lendner JD, Mander BA et al (2019) Bidirectional prefrontal-hippocampal dynamics organize information transfer during sleep in humans. Nat Commun 10:3572

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Mayberg HS, Lozano AM, Voon V et al (2005) Deep brain stimulation for treatment-resistant depression. Neuron 45:651–660

    Article  CAS  PubMed  Google Scholar 

  22. van Westen M, Rietveld E, Figee M et al (2015) Clinical outcome and mechanisms of deep brain stimulation for obsessive-compulsive disorder. Curr Behav Neurosci Rep 2:41–48

    Article  PubMed  PubMed Central  Google Scholar 

  23. Crone NE, Miglioretti DL, Gordon B et al (1998) Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain 121(Pt 12):2301–2315

    Article  PubMed  Google Scholar 

  24. Edwards E, Soltani M, Deouell LY et al (2005) High gamma activity in response to deviant auditory stimuli recorded directly from human cortex. J Neurophysiol 94:4269–4280

    Article  PubMed  Google Scholar 

  25. Ray S, Maunsell JHR (2011) Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol 9:e1000610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Flinker A, Korzeniewska A, Shestyuk AY et al (2015) Redefining the role of Broca’s area in speech. Proc Natl Acad Sci U S A 112:2871–2875

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Dubey A, Ray S (2019) Cortical electrocorticogram (ECoG) is a local signal. J Neurosci 39:4299–4311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Leszczyński M, Barczak A, Kajikawa Y et al (2020) Dissociation of broadband high-frequency activity and neuronal firing in the neocortex. Sci Adv 6:eabb0977

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Holdgraf CR, de Heer W, Pasley B et al (2016) Rapid tuning shifts in human auditory cortex enhance speech intelligibility. Nat Commun 7:13654

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Li G, Jiang S, Paraskevopoulou SE et al (2018) Optimal referencing for stereo-electroencephalographic (SEEG) recordings. NeuroImage 183:327–335

    Article  PubMed  Google Scholar 

  31. Kanth ST, Ray S (2020) Electrocorticogram (ECoG) is highly informative in primate visual cortex. J Neurosci 40:2430–2444

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Johnson EL, Knight RT (2015) Intracranial recordings and human memory. Curr Opin Neurobiol 31:18–25

    Article  CAS  PubMed  Google Scholar 

  33. Eichenbaum H (2017) Prefrontal-hippocampal interactions in episodic memory. Nat Rev Neurosci 18:547–558

    Article  CAS  PubMed  Google Scholar 

  34. Zheng J, Anderson KL, Leal SL et al (2017) Amygdala-hippocampal dynamics during salient information processing. Nat Commun 8:14413

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhang H, Fell J, Axmacher N (2018) Electrophysiological mechanisms of human memory consolidation. Nat Commun 9:4103

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Vaz AP, Inati SK, Brunel N et al (2019) Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory. Science 363:975–978

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Rutishauser U (2019) Testing models of human declarative memory at the single-neuron level. Trend Cog Sci 23:510–524

    Article  Google Scholar 

  38. Fried I, Rutishauser U, Cerf M et al (2014) Single neuron studies of the human brain: probing cognition. MIT Press

    Book  Google Scholar 

  39. Cash SS, Hochberg LR (2015) The emergence of single neurons in clinical neurology. Neuron 86:79–91

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Groves DA, Brown VJ (2005) Vagal nerve stimulation: a review of its applications and potential mechanisms that mediate its clinical effects. Neurosci Biobehav Rev 29:493–500

    Article  PubMed  Google Scholar 

  41. Boëx C, Seeck M, Vulliémoz S et al (2011) Chronic deep brain stimulation in mesial temporal lobe epilepsy. Seizure 20:485–490

    Article  PubMed  Google Scholar 

  42. Miatton M, Van Roost D, Thiery E et al (2011) The cognitive effects of amygdalohippocampal deep brain stimulation in patients with temporal lobe epilepsy. Epilepsy Behav 22:759–764

    Article  PubMed  Google Scholar 

  43. Carrette S, Boon P, Sprengers M et al (2015) Responsive neurostimulation in epilepsy. Expert Rev Neurother 15:1445–1454

    Article  CAS  PubMed  Google Scholar 

  44. Noachtar S, Borggraefe I (2009) Epilepsy surgery: a critical review. Epilepsy Behav 15:66–72

    Article  PubMed  Google Scholar 

  45. Devinsky O, Vezzani A, O’Brien TJ et al (2018) Epilepsy 4:1–24

    Google Scholar 

  46. Rao VR, Lowenstein DH (2015) Epilepsy. Curr Biol 25:R742–R746

    Article  CAS  PubMed  Google Scholar 

  47. Gelinas JN, Khodagholy D, Thesen T et al (2016) Interictal epileptiform discharges induce hippocampal-cortical coupling in temporal lobe epilepsy. Nat Med 22:641–648

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Meisel C (2020) Antiepileptic drugs induce subcritical dynamics in human cortical networks. Proc Natl Acad Sci U S A 117:11118–11125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Talairach J, Bancaud J, Bonis A et al (1962) Functional stereotaxic exploration of epilepsy. Confin Neruol 22:328–331

    Article  CAS  Google Scholar 

  50. Tandon N, Tong BA, Friedman ER et al (2019) Analysis of morbidity and outcomes associated with use of subdural grids vs stereoelectroencephalography in patients with intractable epilepsy. JAMA Neurol 76:672–681

    Article  PubMed  PubMed Central  Google Scholar 

  51. Schalk G, McFarland DJ, Hinterberger T et al (2004) BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 51:1034–1043

    Article  PubMed  Google Scholar 

  52. Kam JWY, Szczepanski SM, Canolty RT, et al (2016) Differential sources for 2 neural signatures of target detection: an electrocorticography study. Cereb Cortex 28(1):9–20

    Google Scholar 

  53. Gray CM, König P, Engel AK et al (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338:334–337

    Article  CAS  PubMed  Google Scholar 

  54. Miller KJ, Sorensen LB, Ojemann JG et al (2009) Power-law scaling in the brain surface electric potential. PLoS Comput Biol 5:e1000609

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Miller KJ, Zanos S, Fetz EE et al (2009) Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. J Neurosci 29:3132–3137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Canolty RT, Edwards E, Dalal SS et al (2006) High gamma power is phase-locked to theta oscillations in human neocortex. Science 313:1626–1628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Brunet N, Vinck M, Bosman CA et al (2014) Gamma or no gamma, that is the question. Trends Cogn Sci (Regul Ed) 18:507–509

    Article  Google Scholar 

  58. Hermes D, Miller KJ, Wandell BA et al (2015) Gamma oscillations in visual cortex: the stimulus matters. Trends Cogn Sci (Regul Ed) 19:57–58

    Article  Google Scholar 

  59. Ray S, Maunsell JHR (2015) Do gamma oscillations play a role in cerebral cortex? Trends Cogn Sci (Regul Ed) 19:78–85

    Article  Google Scholar 

  60. Bruns A (2004) Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches? J Neurosci Methods 137:321–332

    Article  PubMed  Google Scholar 

  61. Foster BL, Rangarajan V, Shirer WR et al (2015) Intrinsic and task-dependent coupling of neuronal population activity in human parietal cortex. Neuron 86:578–590

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Haller M, Case J, Crone NE et al (2018) Persistent neuronal activity in human prefrontal cortex links perception and action. Nat Human Behav 2:80–91

    Article  Google Scholar 

  63. Voytek B, Kayser AS, Badre D et al (2015) Oscillatory dynamics coordinating human frontal networks in support of goal maintenance. Nat Neurosci 18:1318–1324

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Donoghue T, Haller M, Peterson EJ et al (2020) Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci 23:1655–1665

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bastos AM, Schoffelen J-M (2015) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci 9:175

    PubMed  Google Scholar 

  66. Aru J, Aru J, Priesemann V et al (2015) Untangling cross-frequency coupling in neuroscience. Curr Opin Neurobiol 31:51–61

    Article  CAS  PubMed  Google Scholar 

  67. Canolty RT, Knight RT (2010) The functional role of cross-frequency coupling. Trends Cogn Sci (Regul Ed) 14:506–515

    Article  Google Scholar 

  68. Gerber EM, Sadeh B, Ward A et al (2016) Non-sinusoidal activity can produce cross-frequency coupling in cortical signals in the absence of functional interaction between neural sources. PLoS One 11:e0167351

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Tort ABL, Kramer MA, Thorn C et al (2008) Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task. Proc Natl Acad Sci U S A 105:20517–20522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Quian Quiroga R, Panzeri S (2009) Extracting information from neuronal populations: information theory and decoding approaches. Nat Rev Neurosci 10:173–185

    Article  CAS  PubMed  Google Scholar 

  71. Vyas S, Golub MD, Sussillo D et al (2020) Computation through neural population dynamics. Annu Rev Neurosci 43:249–275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Stokes MG, Kusunoki M, Sigala N et al (2013) Dynamic coding for cognitive control in prefrontal cortex. Neuron 78:364–375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Stokes MG (2015) “Activity-silent” working memory in prefrontal cortex: a dynamic coding framework. Trends Cogn Sci (Regul Ed) 19:394–405

    Article  Google Scholar 

  74. Cunningham JP, Byron MY (2014) Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17:1500–1509

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Kobak D, Brendel W, Constantinidis C et al (2016) Demixed principal component analysis of neural population data. eLife 5:e10989

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Kriegeskorte N, Mur M, Bandettini P (2008) Representational similarity analysis - connecting the branches of systems neuroscience. Front Syst Neurosci 2:4

    PubMed  PubMed Central  Google Scholar 

  77. Timme NM, Lapish C (2018) A tutorial for information theory in neuroscience. eNeuro 11:5(3):ENEURO.0052-18.2018. doi: 10.1523/ENEURO.0052-18.2018. PMID: 30211307; PMCID: PMC6131830

    Google Scholar 

  78. Borchers S, Himmelbach M, Logothetis N et al (2012) Direct electrical stimulation of human cortex—the gold standard for mapping brain functions? Nat Rev Neurosci 13:63

    Article  CAS  Google Scholar 

  79. Foster BL, Parvizi J (2017) Direct cortical stimulation of human posteromedial cortex. Neurology 88:685–691

    Article  PubMed  PubMed Central  Google Scholar 

  80. Fox KCR, Shi L, Baek S et al (2020) Intrinsic network architecture predicts the effects elicited by intracranial electrical stimulation of the human brain. Nat Hum Behav 4:1039–1052

    Article  PubMed  PubMed Central  Google Scholar 

  81. Suthana N, Haneef Z, Stern J et al (2012) Memory enhancement and deep-brain stimulation of the entorhinal area. N Engl J Med 366:502–510

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Titiz AS, Hill MRH, Mankin EA et al (2017) Theta-burst microstimulation in the human entorhinal area improves memory specificity. eLife 6:e29515

    Article  PubMed  PubMed Central  Google Scholar 

  83. Hermes D, Miller KJ, Noordmans HJ et al (2010) Automated electrocorticographic electrode localization on individually rendered brain surfaces. J Neurosci Methods 185:293–298

    Article  PubMed  Google Scholar 

  84. Groppe DM, Bickel S, Dykstra AR et al (2017) iELVis: an open source MATLAB toolbox for localizing and visualizing human intracranial electrode data. J Neurosci Methods 281:40–48

    Article  CAS  PubMed  Google Scholar 

  85. Blenkmann AO, Phillips HN, Princich JP et al (2017) iElectrodes: a comprehensive open-source toolbox for depth and subdural grid electrode localization. Front Neuroinform 11:14

    Article  PubMed  PubMed Central  Google Scholar 

  86. Dykstra AR, Chan AM, Quinn BT et al (2012) Individualized localization and cortical surface-based registration of intracranial electrodes. NeuroImage 59:3563–3570

    Article  PubMed  Google Scholar 

  87. Stolk A, Griffin S, van der Meij R et al (2018) Integrated analysis of anatomical and electrophysiological human intracranial data. Nat Protoc 13(7):1699–1723

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Oostenveld R, Fries P, Maris E et al (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011:156869

    Article  PubMed  Google Scholar 

  89. Holdgraf C, Appelhoff S, Bickel S et al (2019) iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Sci Data 6:102

    Article  PubMed  PubMed Central  Google Scholar 

  90. Faraut MC, Carlson AA, Sullivan S et al (2018) Dataset of human medial temporal lobe single neuron activity during declarative memory encoding and recognition. Sci Data 5:180010

    Article  PubMed  PubMed Central  Google Scholar 

  91. Boran E, Fedele T, Steiner A et al (2020) Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task. Sci Data 7:1–7

    Article  Google Scholar 

  92. Johnson EL, Helfrich RF (2016) How brain cells make memories. Front Young Minds 4

    Google Scholar 

  93. Ram B, Helfrich RF Waves of percetion. Front Young Minds 5

    Google Scholar 

  94. Chiong W, Leonard MK, Chang EF (2018) Neurosurgical patients as human research subjects: ethical considerations in intracranial electrophysiology research. Neurosurgery 83:29–37

    Article  PubMed  Google Scholar 

  95. Ammanuel SG, Kleen JK, Leonard MK et al (2020) Interictal epileptiform discharges and the quality of human intracranial neurophysiology data. Front Hum Neurosci 14:44

    Article  PubMed  PubMed Central  Google Scholar 

  96. Buzsáki G (2015) Hippocampal sharp wave-ripple: a cognitive biomarker for episodic memory and planning. Hippocampus 25:1073–1188

    Article  PubMed  PubMed Central  Google Scholar 

  97. Szczepanski SM, Crone NE, Kuperman RA et al (2014) Dynamic changes in phase-amplitude coupling facilitate spatial attention control in fronto-parietal cortex. PLoS Biol 12:e1001936

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Saxe R, Brett M, Kanwisher N (2006) Divide and conquer: a defense of functional localizers. NeuroImage 30:1088–1096

    Article  PubMed  Google Scholar 

  99. Anumanchipalli GK, Chartier J, Chang EF (2019) Speech synthesis from neural decoding of spoken sentences. Nature 568:493–498

    Article  CAS  PubMed  Google Scholar 

  100. Pasley BN, David SV, Mesgarani N et al (2012) Reconstructing speech from human auditory cortex. PLoS Biol 10:e1001251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Oganian Y, Chang EF (2018) A speech envelope landmark for syllable encoding in human superior temporal gyrus. Sci Adv 5:388280

    Google Scholar 

  102. Fiebelkorn IC, Pinsk MA, Kastner S (2018) A dynamic interplay within the Frontoparietal network underlies rhythmic spatial attention. Neuron 99:842–853.e8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by the German Research Foundation (DFG, HE 8329/2-1), the Hertie Foundation (Hertie Network of Excellence in Clinical Neuroscience), and the Baden-Württemberg Foundation.

Author information

Authors and Affiliations

  1. Hertie Institute for Clinical Brain Research, Center for Neurology, University of Tübingen, Tübingen, Germany

    Randolph F. Helfrich

Authors
  1. Randolph F. Helfrich
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ctr for Complex Systems & Brain Sci, Florida Atlantic University, Boca Raton, FL, USA

    Robert P Vertes

  2. Department of Psychology, Florida International University, Miami, FL, USA

    Timothy Allen

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Helfrich, R.F. (2022). Human Intracranial Cognitive Neurophysiology. In: Vertes, R.P., Allen, T. (eds) Electrophysiological Recording Techniques. Neuromethods, vol 192. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2631-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2631-3_10

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2630-6

  • Online ISBN: 978-1-0716-2631-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation